'''simple docstring'''




from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
						import numpy as np
						import tensorflow as tf

						from transformers import (
						    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
						    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
						    TF_MODEL_FOR_MASKED_LM_MAPPING,
						    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
						    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
						    TF_MODEL_FOR_PRETRAINING_MAPPING,
						    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
						    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
						    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
						    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
						    TFSharedEmbeddings,
						)

						if _tf_gpu_memory_limit is not None:
												__lowercase				   =  tf.config.list_physical_devices('''GPU''')
												for gpu in gpus:
																		# Restrict TensorFlow to only allocate x GB of memory on the GPUs
																		try:
																								tf.config.set_logical_device_configuration(
																								    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
																								)
																								__lowercase				   =  tf.config.list_logical_devices('''GPU''')
																								print('''Logical GPUs''', logical_gpus)
																		except RuntimeError as e:
																								# Virtual devices must be set before GPUs have been initialized
																								print(e)






@require_tf
class     a__:


			'''simple docstring'''






			UpperCAmelCase_      :					Dict								=	None
			UpperCAmelCase_      :					List[str]								=	()
			UpperCAmelCase_      :					Union[str, Any]								=	()
			UpperCAmelCase_      :					Union[str, Any]								=	True
			UpperCAmelCase_      :					Dict								=	True
			UpperCAmelCase_      :					Tuple								=	True
			UpperCAmelCase_      :					Any								=	False



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase=False):




					"""simple docstring"""


					lowerCAmelCase        =  copy.deepcopy(__lowerCAmelCase)

					if model_class in get_values(__lowerCAmelCase):
							lowerCAmelCase        =  {
							    k: tf.tile(tf.expand_dims(__lowerCAmelCase	,   1)	,   (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1))
							    if isinstance(__lowerCAmelCase	,   tf.Tensor) and v.ndim > 0
							    else v
							    for k, v in inputs_dict.items()
							}

					if return_labels:
							if model_class in get_values(__lowerCAmelCase):
									lowerCAmelCase        =  tf.ones(self.model_tester.batch_size	,   dtype=tf.intaa)
							elif model_class in get_values(__lowerCAmelCase):
									lowerCAmelCase        =  tf.zeros(self.model_tester.batch_size	,   dtype=tf.intaa)
									lowerCAmelCase        =  tf.zeros(self.model_tester.batch_size	,   dtype=tf.intaa)
							elif model_class in [
							    *get_values(__lowerCAmelCase),
							    *get_values(__lowerCAmelCase),
							]:
									lowerCAmelCase        =  tf.zeros(self.model_tester.batch_size	,   dtype=tf.intaa)
							elif model_class in get_values(__lowerCAmelCase):
									lowerCAmelCase        =  tf.zeros(self.model_tester.batch_size	,   dtype=tf.intaa)
							elif model_class in [
							    *get_values(__lowerCAmelCase),
							    *get_values(__lowerCAmelCase),
							    *get_values(__lowerCAmelCase),
							    *get_values(__lowerCAmelCase),
							    *get_values(__lowerCAmelCase),
							]:
									lowerCAmelCase        =  tf.zeros(
									    (self.model_tester.batch_size, self.model_tester.seq_length)	,   dtype=tf.intaa)
					return inputs_dict



			@slow
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs_for_common()
					for model_class in self.all_model_classes[:2]:
							lowerCAmelCase        =  self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase)
							lowerCAmelCase        =  model_class(__lowerCAmelCase)

							@tf.function
							def run_in_graph_mode():
									return model(__lowerCAmelCase)

							lowerCAmelCase        =  run_in_graph_mode()
							self.assertIsNotNone(__lowerCAmelCase)



			@slow
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs_for_common()
					for model_class in self.all_model_classes[:2]:
							lowerCAmelCase        =  self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase)
							lowerCAmelCase        =  model_class(__lowerCAmelCase)

							@tf.function(experimental_compile=__lowerCAmelCase)
							def run_in_graph_mode():
									return model(__lowerCAmelCase)

							lowerCAmelCase        =  run_in_graph_mode()
							self.assertIsNotNone(__lowerCAmelCase)



			@slow
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs_for_common()
					for model_class in self.all_model_classes[:2]:
							lowerCAmelCase        =  model_class(__lowerCAmelCase)
							if getattr(__lowerCAmelCase	,   """hf_compute_loss"""	,   __lowerCAmelCase):
									# Test that model correctly compute the loss with kwargs
									lowerCAmelCase        =  self._prepare_for_class(inputs_dict.copy()	,   __lowerCAmelCase	,   return_labels=__lowerCAmelCase)
									# Is there a better way to remove these decoder inputs?
									lowerCAmelCase        =  {
									    key: val
									    for key, val in prepared_for_class.items()
									    if key not in ("""head_mask""", """decoder_head_mask""", """cross_attn_head_mask""", """decoder_input_ids""")
									}

									lowerCAmelCase        =  {
									    """labels""",
									    """label""",
									    """label_ids""",
									    """start_positions""",
									    """start_position""",
									    """end_positions""",
									    """end_position""",
									    """next_sentence_label""",
									}
									lowerCAmelCase        =  possible_label_cols.intersection(set(__lowerCAmelCase))
									self.assertGreater(len(__lowerCAmelCase)	,   0	,   msg="""No matching label names found!""")
									lowerCAmelCase        =  {key: val for key, val in prepared_for_class.items() if key in label_names}
									lowerCAmelCase        =  {key: val for key, val in prepared_for_class.items() if key not in label_names}
									self.assertGreater(len(__lowerCAmelCase)	,   0)

									# Make sure it works with XLA!
									model.compile(optimizer=tf.keras.optimizers.SGD(0.0)	,   jit_compile=__lowerCAmelCase)
									# Make sure the model fits without crashing regardless of where we pass the labels
									lowerCAmelCase        =  model.fit(
									    __lowerCAmelCase	,   validation_data=__lowerCAmelCase	,   steps_per_epoch=1	,   validation_steps=1	,   shuffle=__lowerCAmelCase	,   verbose=0	,   )
									lowerCAmelCase        =  history.history["""loss"""][0]
									self.assertTrue(not isnan(__lowerCAmelCase))
									lowerCAmelCase        =  history.history["""val_loss"""][0]
									self.assertTrue(not isnan(__lowerCAmelCase))

									# Now test it with separate labels, to make sure that path works in XLA too.
									lowerCAmelCase        =  model_class(__lowerCAmelCase)
									model.compile(optimizer=tf.keras.optimizers.SGD(0.0)	,   jit_compile=__lowerCAmelCase)
									lowerCAmelCase        =  model.fit(
									    __lowerCAmelCase	,   __lowerCAmelCase	,   validation_data=(inputs_minus_labels, labels)	,   steps_per_epoch=1	,   validation_steps=1	,   shuffle=__lowerCAmelCase	,   verbose=0	,   )

									lowerCAmelCase        =  history.history["""loss"""][0]
									self.assertTrue(not isnan(__lowerCAmelCase))
									lowerCAmelCase        =  history.history["""val_loss"""][0]
									self.assertTrue(not isnan(__lowerCAmelCase))



			@slow
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs_for_common()
					lowerCAmelCase        =  True
					lowerCAmelCase        =  True

					if hasattr(__lowerCAmelCase	,   """use_cache"""):
							lowerCAmelCase        =  True

					lowerCAmelCase        =  getattr(self.model_tester	,   """encoder_seq_length"""	,   self.model_tester.seq_length)
					lowerCAmelCase        =  getattr(self.model_tester	,   """key_length"""	,   __lowerCAmelCase)

					for model_class in self.all_model_classes[:2]:
							lowerCAmelCase        =  self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase)
							lowerCAmelCase        =  model_class(__lowerCAmelCase)
							model.build()
							lowerCAmelCase        =  len(model(__lowerCAmelCase))

							for key in list(class_inputs_dict.keys()):
									# Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
									if key not in model.input_signature:
											del class_inputs_dict[key]
									# Check it's a tensor, in case the inputs dict has some bools in it too
									elif isinstance(class_inputs_dict[key]	,   tf.Tensor) and class_inputs_dict[key].dtype.is_integer:
											lowerCAmelCase        =  tf.cast(class_inputs_dict[key]	,   tf.intaa)

							if set(class_inputs_dict.keys()) != set(model.input_signature.keys()):
									continue  # Some models have inputs that the preparation functions don't create, we skip those

							with tempfile.TemporaryDirectory() as tmpdirname:
									model.save_pretrained(__lowerCAmelCase	,   saved_model=__lowerCAmelCase)
									lowerCAmelCase        =  os.path.join(__lowerCAmelCase	,   """saved_model"""	,   """1""")
									lowerCAmelCase        =  tf.keras.models.load_model(__lowerCAmelCase)
									lowerCAmelCase        =  model(__lowerCAmelCase)

									if self.is_encoder_decoder:
											lowerCAmelCase        =  outputs["""encoder_hidden_states"""]
											lowerCAmelCase        =  outputs["""encoder_attentions"""]
									else:
											lowerCAmelCase        =  outputs["""hidden_states"""]
											lowerCAmelCase        =  outputs["""attentions"""]

									self.assertEqual(len(__lowerCAmelCase)	,   __lowerCAmelCase)

									lowerCAmelCase        =  getattr(
									    self.model_tester	,   """expected_num_hidden_layers"""	,   self.model_tester.num_hidden_layers + 1)

									self.assertEqual(len(__lowerCAmelCase)	,   __lowerCAmelCase)
									self.assertListEqual(
									    list(output_hidden_states[0].shape[-2:])	,   [self.model_tester.seq_length, self.model_tester.hidden_size]	,   )

									self.assertEqual(len(__lowerCAmelCase)	,   self.model_tester.num_hidden_layers)
									self.assertListEqual(
									    list(output_attentions[0].shape[-3:])	,   [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]	,   )



			@slow
			def 							a_	(       self):




					"""simple docstring"""


					tf.keras.mixed_precision.set_global_policy("""mixed_float16""")

					# try/finally block to ensure subsequent tests run in float32
					try:
							lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes[:2]:
									lowerCAmelCase        =  self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase)
									lowerCAmelCase        =  model_class(__lowerCAmelCase)
									lowerCAmelCase        =  model(__lowerCAmelCase)

									self.assertIsNotNone(__lowerCAmelCase)
					finally:
							tf.keras.mixed_precision.set_global_policy("""float32""")



			@slow
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs_for_common()
					# head_mask and decoder_head_mask has different shapes than other input args
					if "head_mask" in inputs_dict:
							del inputs_dict["head_mask"]
					if "decoder_head_mask" in inputs_dict:
							del inputs_dict["decoder_head_mask"]
					if "cross_attn_head_mask" in inputs_dict:
							del inputs_dict["cross_attn_head_mask"]
					lowerCAmelCase        =  {
					    module_member
					    for model_class in self.all_model_classes
					    for module in (import_module(model_class.__module__),)
					    for module_member_name in dir(__lowerCAmelCase)
					    if module_member_name.endswith("""MainLayer""")
					    for module_member in (getattr(__lowerCAmelCase	,   __lowerCAmelCase),)
					    if isinstance(__lowerCAmelCase	,   __lowerCAmelCase)
					    and tf.keras.layers.Layer in module_member.__bases__
					    and getattr(__lowerCAmelCase	,   """_keras_serializable"""	,   __lowerCAmelCase)
					}

					for main_layer_class in tf_main_layer_classes:
							# T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
							if "T5" in main_layer_class.__name__:
									# Take the same values than in TFT5ModelTester for this shared layer
									lowerCAmelCase        =  TFSharedEmbeddings(self.model_tester.vocab_size	,   self.model_tester.hidden_size	,   name="""shared""")
									lowerCAmelCase        =  False
									lowerCAmelCase        =  main_layer_class(__lowerCAmelCase	,   embed_tokens=__lowerCAmelCase)
							else:
									lowerCAmelCase        =  main_layer_class(__lowerCAmelCase)

							lowerCAmelCase        =  {
							    name: tf.keras.Input(tensor.shape[1:]	,   dtype=tensor.dtype) for name, tensor in inputs_dict.items()
							}

							if hasattr(self.model_tester	,   """num_labels"""):
									lowerCAmelCase        =  self.model_tester.num_labels
							else:
									lowerCAmelCase        =  2

							lowerCAmelCase        =  tf.data.Dataset.from_tensor_slices(
							    (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)))).batch(1)

							lowerCAmelCase        =  main_layer(__lowerCAmelCase)[0]
							lowerCAmelCase        =  tf.keras.layers.Dense(__lowerCAmelCase	,   activation="""softmax"""	,   name="""outputs""")(__lowerCAmelCase)
							lowerCAmelCase        =  tf.keras.models.Model(inputs=__lowerCAmelCase	,   outputs=[outputs])

							model.compile(loss="""binary_crossentropy"""	,   optimizer="""adam"""	,   metrics=["""binary_accuracy"""])
							model.fit(__lowerCAmelCase	,   epochs=1)

							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase        =  os.path.join(__lowerCAmelCase	,   """keras_model.h5""")
									model.save(__lowerCAmelCase)
									if "T5" in main_layer_class.__name__:
											lowerCAmelCase        =  tf.keras.models.load_model(
											    __lowerCAmelCase	,   custom_objects={
											        main_layer_class.__name__: main_layer_class,
											        """TFSharedEmbeddings""": TFSharedEmbeddings,
											    }	,   )
									else:
											lowerCAmelCase        =  tf.keras.models.load_model(
											    __lowerCAmelCase	,   custom_objects={main_layer_class.__name__: main_layer_class})
									assert isinstance(__lowerCAmelCase	,   tf.keras.Model)
									model(__lowerCAmelCase)



			@slow
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes[:2]:
							lowerCAmelCase        =  model_class(__lowerCAmelCase)

							lowerCAmelCase        =  copy.deepcopy(__lowerCAmelCase)

							if not self.is_encoder_decoder:
									lowerCAmelCase        =  inputs["""input_ids"""]
									del inputs["input_ids"]
							else:
									lowerCAmelCase        =  inputs["""input_ids"""]
									lowerCAmelCase        =  inputs.get("""decoder_input_ids"""	,   __lowerCAmelCase)
									del inputs["input_ids"]
									inputs.pop("""decoder_input_ids"""	,   __lowerCAmelCase)

							if not self.is_encoder_decoder:
									lowerCAmelCase        =  model.get_input_embeddings()(__lowerCAmelCase)
							else:
									lowerCAmelCase        =  model.get_input_embeddings()(__lowerCAmelCase)
									lowerCAmelCase        =  model.get_input_embeddings()(__lowerCAmelCase)

							lowerCAmelCase        =  self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase)

							@tf.function
							def run_in_graph_mode():
									return model(__lowerCAmelCase)

							lowerCAmelCase        =  run_in_graph_mode()
							self.assertIsNotNone(__lowerCAmelCase)



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  []
					if model.config.bos_token_id is not None:
							special_tokens.append(model.config.bos_token_id)
					if model.config.pad_token_id is not None:
							special_tokens.append(model.config.pad_token_id)
					if model.config.eos_token_id is not None:
							special_tokens.append(model.config.eos_token_id)

					# create random bad tokens that are not special tokens
					lowerCAmelCase        =  []
					while len(__lowerCAmelCase) < num_bad_tokens:
							lowerCAmelCase        =  tf.squeeze(ids_tensor((1, 1)	,   self.model_tester.vocab_size)	,   0).numpy()[0]
							if token not in special_tokens:
									bad_tokens.append(__lowerCAmelCase)
					return bad_tokens



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					for token_id in output_ids[0].numpy().tolist():
							self.assertGreaterEqual(__lowerCAmelCase	,   0)
							self.assertLess(__lowerCAmelCase	,   self.model_tester.vocab_size)







			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					for bad_word_ids in bad_words_ids:
							# for all slices in batch
							for generated_ids_slice in generated_ids:
									# for all word idx
									for i in range(len(__lowerCAmelCase)	,   len(__lowerCAmelCase)):
											# if tokens match
											if generated_ids_slice[i - len(__lowerCAmelCase) : i] == bad_word_ids:
													return True
					return False





'''simple docstring'''




import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
						import tensorflow as tf

						tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
						import torch

if is_datasets_available():
						from datasets import load_dataset


__lowercase				   =  5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors






def     snake_case__							(      _A: Namespace			)   ->     str:

		'''simple docstring'''


		return PTtoTFCommand(
		    args.model_name				,       args.local_dir				,       args.max_error				,       args.new_weights				,       args.no_pr				,       args.push				,       args.extra_commit_description				,       args.override_model_class				,       )






class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			@staticmethod
			def 							a_	(       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  parser.add_parser(
					    """pt-to-tf"""	,   help=(
					        """CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."""
					        """ Can also be used to validate existing weights without opening PRs, with --no-pr."""
					    )	,   )
					train_parser.add_argument(
					    """--model-name"""	,   type=__lowerCAmelCase	,   required=__lowerCAmelCase	,   help="""The model name, including owner/organization, as seen on the hub."""	,   )
					train_parser.add_argument(
					    """--local-dir"""	,   type=__lowerCAmelCase	,   default=""""""	,   help="""Optional local directory of the model repository. Defaults to /tmp/{model_name}"""	,   )
					train_parser.add_argument(
					    """--max-error"""	,   type=__lowerCAmelCase	,   default=__lowerCAmelCase	,   help=(
					        f"Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."
					    )	,   )
					train_parser.add_argument(
					    """--new-weights"""	,   action="""store_true"""	,   help="""Optional flag to create new TensorFlow weights, even if they already exist."""	,   )
					train_parser.add_argument(
					    """--no-pr"""	,   action="""store_true"""	,   help="""Optional flag to NOT open a PR with converted weights.""")
					train_parser.add_argument(
					    """--push"""	,   action="""store_true"""	,   help="""Optional flag to push the weights directly to `main` (requires permissions)"""	,   )
					train_parser.add_argument(
					    """--extra-commit-description"""	,   type=__lowerCAmelCase	,   default=""""""	,   help="""Optional additional commit description to use when opening a PR (e.g. to tag the owner)."""	,   )
					train_parser.add_argument(
					    """--override-model-class"""	,   type=__lowerCAmelCase	,   default=__lowerCAmelCase	,   help="""If you think you know better than the auto-detector, you can specify the model class here. """
					    """Can be either an AutoModel class or a specific model class like BertForSequenceClassification."""	,   )
					train_parser.set_defaults(func=__lowerCAmelCase)



			@staticmethod
			def 							a_	(       __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  set(pt_outputs.keys())
					lowerCAmelCase        =  set(tf_outputs.keys())
					if pt_out_attrs != tf_out_attrs:
							raise ValueError(
							    f"The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"
							    f" {tf_out_attrs})")

					# 2. For each output attribute, computes the difference
					def _find_pt_tf_differences(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase=""):
							# If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
							# recursivelly, keeping the name of the attribute.
							if isinstance(__lowerCAmelCase	,   torch.Tensor):
									lowerCAmelCase        =  np.max(np.abs(pt_out.numpy() - tf_out.numpy()))
									lowerCAmelCase        =  tensor_difference
							else:
									lowerCAmelCase        =  attr_name
									for i, pt_item in enumerate(__lowerCAmelCase):
											# If it is a named attribute, we keep the name. Otherwise, just its index.
											if isinstance(__lowerCAmelCase	,   __lowerCAmelCase):
													lowerCAmelCase        =  root_name + pt_item
													lowerCAmelCase        =  tf_out[pt_item]
													lowerCAmelCase        =  pt_out[pt_item]
											else:
													lowerCAmelCase        =  root_name + f"[{i}]"
													lowerCAmelCase        =  tf_out[i]
											lowerCAmelCase        =  _find_pt_tf_differences(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase)

							return differences

					return _find_pt_tf_differences(__lowerCAmelCase	,   __lowerCAmelCase	,   {})



			def __init__(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   *__lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  logging.get_logger("""transformers-cli/pt_to_tf""")
					lowerCAmelCase        =  model_name
					lowerCAmelCase        =  local_dir if local_dir else os.path.join("""/tmp"""	,   __lowerCAmelCase)
					lowerCAmelCase        =  max_error
					lowerCAmelCase        =  new_weights
					lowerCAmelCase        =  no_pr
					lowerCAmelCase        =  push
					lowerCAmelCase        =  extra_commit_description
					lowerCAmelCase        =  override_model_class



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					def _get_audio_input():
							lowerCAmelCase        =  load_dataset("""hf-internal-testing/librispeech_asr_dummy"""	,   """clean"""	,   split="""validation""")
							lowerCAmelCase        =  ds.sort("""id""").select(range(2))[:2]["""audio"""]
							lowerCAmelCase        =  [x["""array"""] for x in speech_samples]
							return raw_samples

					lowerCAmelCase        =  type(pt_model.config)
					if model_config_class in PROCESSOR_MAPPING:
							lowerCAmelCase        =  AutoProcessor.from_pretrained(self._local_dir)
							if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
									lowerCAmelCase        =  processor.tokenizer.eos_token
					elif model_config_class in IMAGE_PROCESSOR_MAPPING:
							lowerCAmelCase        =  AutoImageProcessor.from_pretrained(self._local_dir)
					elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
							lowerCAmelCase        =  AutoFeatureExtractor.from_pretrained(self._local_dir)
					elif model_config_class in TOKENIZER_MAPPING:
							lowerCAmelCase        =  AutoTokenizer.from_pretrained(self._local_dir)
							if processor.pad_token is None:
									lowerCAmelCase        =  processor.eos_token
					else:
							raise ValueError(f"Unknown data processing type (model config type: {model_config_class})")

					lowerCAmelCase        =  set(inspect.signature(pt_model.forward).parameters.keys())
					lowerCAmelCase        =  {}
					if "input_ids" in model_forward_signature:
							processor_inputs.update(
							    {
							        """text""": ["""Hi there!""", """I am a batch with more than one row and different input lengths."""],
							        """padding""": True,
							        """truncation""": True,
							    })
					if "pixel_values" in model_forward_signature:
							lowerCAmelCase        =  load_dataset("""cifar10"""	,   """plain_text"""	,   split="""test""")[:2]["""img"""]
							processor_inputs.update({"""images""": sample_images})
					if "input_features" in model_forward_signature:
							lowerCAmelCase        =  inspect.signature(processor.feature_extractor).parameters
							# Pad to the largest input length by default but take feature extractor default
							# padding value if it exists e.g. "max_length" and is not False or None
							if "padding" in feature_extractor_signature:
									lowerCAmelCase        =  feature_extractor_signature["""padding"""].default
									if default_strategy is not False and default_strategy is not None:
											lowerCAmelCase        =  default_strategy
									else:
											lowerCAmelCase        =  True
							else:
									lowerCAmelCase        =  True
							processor_inputs.update({"""audio""": _get_audio_input(), """padding""": padding_strategy})
					if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
							processor_inputs.update({"""audio""": _get_audio_input(), """padding""": True})
					lowerCAmelCase        =  processor(**__lowerCAmelCase	,   return_tensors="""pt""")
					lowerCAmelCase        =  processor(**__lowerCAmelCase	,   return_tensors="""tf""")

					# Extra input requirements, in addition to the input modality
					if (
					    config.is_encoder_decoder
					    or (hasattr(__lowerCAmelCase	,   """encoder""") and hasattr(__lowerCAmelCase	,   """decoder"""))
					    or "decoder_input_ids" in tf_dummy_inputs
					):
							lowerCAmelCase        =  np.asarray([[1], [1]]	,   dtype=__lowerCAmelCase) * (pt_model.config.decoder_start_token_id or 0)
							pt_input.update({"""decoder_input_ids""": torch.tensor(__lowerCAmelCase)})
							tf_input.update({"""decoder_input_ids""": tf.convert_to_tensor(__lowerCAmelCase)})

					return pt_input, tf_input







			def 							a_	(       self):




					"""simple docstring"""


					if version.parse(huggingface_hub.__version__) < version.parse("""0.9.0"""):
							raise ImportError(
							    """The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"""
							    """ installation.""")
					else:
							from huggingface_hub import Repository, create_commit
							from huggingface_hub._commit_api import CommitOperationAdd

					# Fetch remote data
					lowerCAmelCase        =  Repository(local_dir=self._local_dir	,   clone_from=self._model_name)

					# Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
					lowerCAmelCase        =  AutoConfig.from_pretrained(self._local_dir)
					lowerCAmelCase        =  config.architectures
					if self._override_model_class is not None:
							if self._override_model_class.startswith("""TF"""):
									lowerCAmelCase        =  [self._override_model_class[2:]]
							else:
									lowerCAmelCase        =  [self._override_model_class]
							try:
									lowerCAmelCase        =  getattr(import_module("""transformers""")	,   architectures[0])
							except AttributeError:
									raise ValueError(f"Model class {self._override_model_class} not found in transformers.")
							try:
									lowerCAmelCase        =  getattr(import_module("""transformers""")	,   """TF""" + architectures[0])
							except AttributeError:
									raise ValueError(f"TF model class TF{self._override_model_class} not found in transformers.")
					elif architectures is None:  # No architecture defined -- use auto classes
							lowerCAmelCase        =  getattr(import_module("""transformers""")	,   """AutoModel""")
							lowerCAmelCase        =  getattr(import_module("""transformers""")	,   """TFAutoModel""")
							self._logger.warning("""No detected architecture, using AutoModel/TFAutoModel""")
					else:  # Architecture defined -- use it
							if len(__lowerCAmelCase) > 1:
									raise ValueError(f"More than one architecture was found, aborting. (architectures = {architectures})")
							self._logger.warning(f"Detected architecture: {architectures[0]}")
							lowerCAmelCase        =  getattr(import_module("""transformers""")	,   architectures[0])
							try:
									lowerCAmelCase        =  getattr(import_module("""transformers""")	,   """TF""" + architectures[0])
							except AttributeError:
									raise AttributeError(f"The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers.")

        # Check the TF dummy inputs to see what keys we need in the forward pass
					lowerCAmelCase        =  tf_class.from_config(__lowerCAmelCase)
					lowerCAmelCase        =  tf_from_pt_model.dummy_inputs

					del tf_from_pt_model  # Try to keep only one model in memory at a time

					# Load the model and get some basic inputs
					lowerCAmelCase        =  pt_class.from_pretrained(self._local_dir)
					pt_model.eval()

					lowerCAmelCase ,					lowerCAmelCase        =  self.get_inputs(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase)

					with torch.no_grad():
							lowerCAmelCase        =  pt_model(**__lowerCAmelCase	,   output_hidden_states=__lowerCAmelCase)
					del pt_model  # will no longer be used, and may have a large memory footprint

					lowerCAmelCase        =  tf_class.from_pretrained(self._local_dir	,   from_pt=__lowerCAmelCase)
					lowerCAmelCase        =  tf_from_pt_model(**__lowerCAmelCase	,   output_hidden_states=__lowerCAmelCase	,   training=__lowerCAmelCase)

					# Confirms that cross loading PT weights into TF worked.
					lowerCAmelCase        =  self.find_pt_tf_differences(__lowerCAmelCase	,   __lowerCAmelCase)
					lowerCAmelCase        =  {k: v for k, v in crossload_differences.items() if """hidden""" not in k}
					lowerCAmelCase        =  {k: v for k, v in crossload_differences.items() if """hidden""" in k}
					if len(__lowerCAmelCase) == 0 and architectures is not None:
							raise ValueError(
							    f"Something went wrong -- the config file has architectures ({architectures}), but no model head"
							    """ output was found. All outputs start with 'hidden'""")
					lowerCAmelCase        =  max(output_differences.values()) if output_differences else 0.0
					lowerCAmelCase        =  max(hidden_differences.values())
					if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
							raise ValueError(
							    """The cross-loaded TensorFlow model has different outputs, something went wrong!\n"""
							    + f"\nList of maximum output differences above the threshold ({self._max_error}):\n"
							    + """\n""".join([f"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error])
							    + f"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
							    + """\n""".join([f"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error]))

					# Save the weights in a TF format (if needed) and confirms that the results are still good
					lowerCAmelCase        =  os.path.join(self._local_dir	,   __lowerCAmelCase)
					lowerCAmelCase        =  os.path.join(self._local_dir	,   __lowerCAmelCase)
					if (not os.path.exists(__lowerCAmelCase) and not os.path.exists(__lowerCAmelCase)) or self._new_weights:
							tf_from_pt_model.save_pretrained(self._local_dir)
					del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

					lowerCAmelCase        =  tf_class.from_pretrained(self._local_dir)
					lowerCAmelCase        =  tf_model(**__lowerCAmelCase	,   output_hidden_states=__lowerCAmelCase)

					lowerCAmelCase        =  self.find_pt_tf_differences(__lowerCAmelCase	,   __lowerCAmelCase)
					lowerCAmelCase        =  {k: v for k, v in conversion_differences.items() if """hidden""" not in k}
					lowerCAmelCase        =  {k: v for k, v in conversion_differences.items() if """hidden""" in k}
					if len(__lowerCAmelCase) == 0 and architectures is not None:
							raise ValueError(
							    f"Something went wrong -- the config file has architectures ({architectures}), but no model head"
							    """ output was found. All outputs start with 'hidden'""")
					lowerCAmelCase        =  max(output_differences.values()) if output_differences else 0.0
					lowerCAmelCase        =  max(hidden_differences.values())
					if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
							raise ValueError(
							    """The converted TensorFlow model has different outputs, something went wrong!\n"""
							    + f"\nList of maximum output differences above the threshold ({self._max_error}):\n"
							    + """\n""".join([f"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error])
							    + f"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
							    + """\n""".join([f"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error]))

					lowerCAmelCase        =  """Update TF weights""" if self._new_weights else """Add TF weights"""
					if self._push:
							repo.git_add(auto_lfs_track=__lowerCAmelCase)
							repo.git_commit(__lowerCAmelCase)
							repo.git_push(blocking=__lowerCAmelCase)  # this prints a progress bar with the upload
							self._logger.warning(f"TF weights pushed into {self._model_name}")
					elif not self._no_pr:
							self._logger.warning("""Uploading the weights into a new PR...""")
							lowerCAmelCase        =  (
							    """Model converted by the [`transformers`' `pt_to_tf`"""
							    """ CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). """
							    """All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n"""
							    f"Maximum crossload output difference={max_crossload_output_diff:.3e}; "
							    f"Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"
							    f"Maximum conversion output difference={max_conversion_output_diff:.3e}; "
							    f"Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"
							)
							if self._max_error > MAX_ERROR:
									commit_descrition += (
									    f"\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"
									)
							if self._extra_commit_description:
									commit_descrition += "\n\n" + self._extra_commit_description

							# sharded model -> adds all related files (index and .h5 shards)
							if os.path.exists(__lowerCAmelCase):
									lowerCAmelCase        =  [
									    CommitOperationAdd(path_in_repo=__lowerCAmelCase	,   path_or_fileobj=__lowerCAmelCase)
									]
									for shard_path in tf.io.gfile.glob(self._local_dir + """/tf_model-*.h5"""):
											operations += [
											    CommitOperationAdd(path_in_repo=os.path.basename(__lowerCAmelCase)	,   path_or_fileobj=__lowerCAmelCase)
											]
							else:
									lowerCAmelCase        =  [CommitOperationAdd(path_in_repo=__lowerCAmelCase	,   path_or_fileobj=__lowerCAmelCase)]

							lowerCAmelCase        =  create_commit(
							    repo_id=self._model_name	,   operations=__lowerCAmelCase	,   commit_message=__lowerCAmelCase	,   commit_description=__lowerCAmelCase	,   repo_type="""model"""	,   create_pr=__lowerCAmelCase	,   ).pr_url
							self._logger.warning(f"PR open in {hub_pr_url}")




