

import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
				import jax
				import jax.numpy as jnp

				from transformers.modeling_flax_pytorch_utils import (
				    convert_pytorch_state_dict_to_flax,
				    load_flax_weights_in_pytorch_model,
				)
				from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
				import torch

class   __UpperCamelCase      :




	def __init__(						self		:  Dict						,		lowerCAmelCase		:  Dict						,		lowerCAmelCase		:  Optional[Any]=14						,		lowerCAmelCase		:  List[Any]=7						,		lowerCAmelCase		:  List[Any]=True						,		lowerCAmelCase		:  str=True						,		lowerCAmelCase		:  Union[str, Any]=False						,		lowerCAmelCase		:  str=True						,		lowerCAmelCase		:  int=99						,		lowerCAmelCase		:  str=32						,		lowerCAmelCase		:  List[Any]=4						,		lowerCAmelCase		:  Union[str, Any]=4						,		lowerCAmelCase		:  Optional[Any]=[[["global", "local"], 2]]						,		lowerCAmelCase		:  int=37						,		lowerCAmelCase		:  Any="gelu"						,		lowerCAmelCase		:  List[Any]=0.1						,		lowerCAmelCase		:  Dict=0.1						,		lowerCAmelCase		:  Tuple=512						,		lowerCAmelCase		:  Tuple=7						,		lowerCAmelCase		:  Any=0.02						,		):


		'''simple docstring'''







		UpperCAmelCase_    			=     parent
		UpperCAmelCase_    			=     batch_size
		UpperCAmelCase_    			=     seq_length
		UpperCAmelCase_    			=     is_training
		UpperCAmelCase_    			=     use_input_mask
		UpperCAmelCase_    			=     use_token_type_ids
		UpperCAmelCase_    			=     use_labels
		UpperCAmelCase_    			=     vocab_size
		UpperCAmelCase_    			=     hidden_size
		UpperCAmelCase_    			=     num_hidden_layers
		UpperCAmelCase_    			=     num_attention_heads
		UpperCAmelCase_    			=     attention_types
		UpperCAmelCase_    			=     intermediate_size
		UpperCAmelCase_    			=     hidden_act
		UpperCAmelCase_    			=     hidden_dropout_prob
		UpperCAmelCase_    			=     attention_probs_dropout_prob
		UpperCAmelCase_    			=     max_position_embeddings
		UpperCAmelCase_    			=     window_size
		UpperCAmelCase_    			=     initializer_range
		UpperCAmelCase_    			=     None
		UpperCAmelCase_    			=     vocab_size - 1
		UpperCAmelCase_    			=     vocab_size - 1
		UpperCAmelCase_    			=     vocab_size - 1




	def  __A   (						self		:  List[str]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     ids_tensor([self.batch_size, self.seq_length]						,		self.vocab_size							)

		UpperCAmelCase_    			=     None
		if self.use_input_mask:
			UpperCAmelCase_    			=     random_attention_mask([self.batch_size, self.seq_length]							)

		UpperCAmelCase_    			=     GPTNeoConfig(
		    vocab_size=self.vocab_size						,		hidden_size=self.hidden_size						,		num_layers=self.num_hidden_layers						,		num_heads=self.num_attention_heads						,		max_position_embeddings=self.max_position_embeddings						,		use_cache=lowerCAmelCase						,		bos_token_id=self.bos_token_id						,		eos_token_id=self.eos_token_id						,		pad_token_id=self.pad_token_id						,		window_size=self.window_size						,		attention_types=self.attention_types						,		)

		return (config, input_ids, input_mask)




	def  __A   (						self		:  Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     self.prepare_config_and_inputs()
		UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_    			=     config_and_inputs
		UpperCAmelCase_    			=     {"input_ids": input_ids, "attention_mask": attention_mask}
		return config, inputs_dict




	def  __A   (						self		:  Tuple						,		lowerCAmelCase		:  Dict						,		lowerCAmelCase		:  Any						,		lowerCAmelCase		:  Any						,		lowerCAmelCase		:  List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     20
		UpperCAmelCase_    			=     model_class_name(lowerCAmelCase							)

		UpperCAmelCase_    			=     model.init_cache(input_ids.shape[0]						,		lowerCAmelCase							)
		UpperCAmelCase_    			=     jnp.ones((input_ids.shape[0], max_decoder_length)						,		dtype="i4"							)

		UpperCAmelCase_    			=     jnp.broadcast_to(
		    jnp.arange(input_ids.shape[-1] - 1							)[None, :]						,		(input_ids.shape[0], input_ids.shape[-1] - 1)							)
		UpperCAmelCase_    			=     model(
		    input_ids[:, :-1]						,		attention_mask=lowerCAmelCase						,		past_key_values=lowerCAmelCase						,		position_ids=lowerCAmelCase						,		)

		UpperCAmelCase_    			=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,		dtype="i4"							)
		UpperCAmelCase_    			=     model(
		    input_ids[:, -1:]						,		attention_mask=lowerCAmelCase						,		past_key_values=outputs_cache.past_key_values						,		position_ids=lowerCAmelCase						,		)

		UpperCAmelCase_    			=     model(lowerCAmelCase							)

		UpperCAmelCase_    			=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
		self.parent.assertTrue(diff < 1e-3						,		msg=F"Max diff is {diff}"							)




	def  __A   (						self		:  List[Any]						,		lowerCAmelCase		:  Optional[Any]						,		lowerCAmelCase		:  Union[str, Any]						,		lowerCAmelCase		:  Optional[Any]						,		lowerCAmelCase		:  Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     20
		UpperCAmelCase_    			=     model_class_name(lowerCAmelCase							)

		UpperCAmelCase_    			=     jnp.concatenate(
		    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])							)]						,		axis=-1						,		)

		UpperCAmelCase_    			=     model.init_cache(input_ids.shape[0]						,		lowerCAmelCase							)
		UpperCAmelCase_    			=     jnp.broadcast_to(
		    jnp.arange(input_ids.shape[-1] - 1							)[None, :]						,		(input_ids.shape[0], input_ids.shape[-1] - 1)							)

		UpperCAmelCase_    			=     model(
		    input_ids[:, :-1]						,		attention_mask=lowerCAmelCase						,		past_key_values=lowerCAmelCase						,		position_ids=lowerCAmelCase						,		)
		UpperCAmelCase_    			=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,		dtype="i4"							)
		UpperCAmelCase_    			=     model(
		    input_ids[:, -1:]						,		past_key_values=outputs_cache.past_key_values						,		attention_mask=lowerCAmelCase						,		position_ids=lowerCAmelCase						,		)

		UpperCAmelCase_    			=     model(lowerCAmelCase						,		attention_mask=lowerCAmelCase							)

		UpperCAmelCase_    			=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
		self.parent.assertTrue(diff < 1e-3						,		msg=F"Max diff is {diff}"							)


@require_flax
class   __UpperCamelCase      (   lowercase       ,       lowercase       ,       unittest.TestCase  ):
	SCREAMING_SNAKE_CASE__      			=       (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
	SCREAMING_SNAKE_CASE__      			=       (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()




	def  __A   (						self		:  Dict							):


		'''simple docstring'''







		UpperCAmelCase_    			=     FlaxGPTNeoModelTester(self							)




	def  __A   (						self		:  int							):


		'''simple docstring'''







		for model_class_name in self.all_model_classes:
			UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_    			=     self.model_tester.prepare_config_and_inputs()
			self.model_tester.check_use_cache_forward(lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase							)




	def  __A   (						self		:  Optional[Any]							):


		'''simple docstring'''







		for model_class_name in self.all_model_classes:
			UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_    			=     self.model_tester.prepare_config_and_inputs()
			self.model_tester.check_use_cache_forward_with_attn_mask(
			    lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase							)




	@slow
	def  __A   (						self		:  Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     GPTaTokenizer.from_pretrained("gpt2"						,		pad_token="<|endoftext|>"						,		padding_side="left"							)
		UpperCAmelCase_    			=     tokenizer(["Hello this is a long string", "Hey"]						,		return_tensors="np"						,		padding=lowerCAmelCase						,		truncation=lowerCAmelCase							)

		UpperCAmelCase_    			=     FlaxGPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M"							)
		UpperCAmelCase_    			=     False
		UpperCAmelCase_    			=     model.config.eos_token_id

		UpperCAmelCase_    			=     jax.jit(model.generate							)

		UpperCAmelCase_    			=     jit_generate(
		    inputs["input_ids"]						,		attention_mask=inputs["attention_mask"]						,		pad_token_id=tokenizer.pad_token_id							).sequences

		UpperCAmelCase_    			=     tokenizer.batch_decode(lowerCAmelCase						,		skip_special_tokens=lowerCAmelCase							)

		UpperCAmelCase_    			=     [
		    "Hello this is a long string of text.\n\nI'm trying to get the text of the",
		    "Hey, I'm a little late to the party. I'm going to",
		]

		self.assertListEqual(lowerCAmelCase						,		lowerCAmelCase							)




	@is_pt_flax_cross_test
	def  __A   (						self		:  List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_			,				UpperCAmelCase_    			=     self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			with self.subTest(model_class.__name__							):
				# prepare inputs
				UpperCAmelCase_    			=     self._prepare_for_class(lowerCAmelCase						,		lowerCAmelCase							)
				UpperCAmelCase_    			=     {k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

				# load corresponding PyTorch class
				UpperCAmelCase_    			=     model_class.__name__[4:]  # Skip the "Flax" at the beginning
				UpperCAmelCase_    			=     getattr(lowerCAmelCase						,		lowerCAmelCase							)

				UpperCAmelCase_			,				UpperCAmelCase_    			=     pt_inputs["input_ids"].shape
				UpperCAmelCase_    			=     np.random.randint(0						,		seq_length - 1						,		size=(batch_size,)							)
				for batch_idx, start_index in enumerate(lowerCAmelCase							):
					UpperCAmelCase_    			=     0
					UpperCAmelCase_    			=     1
					UpperCAmelCase_    			=     0
					UpperCAmelCase_    			=     1
				UpperCAmelCase_    			=     pt_model_class(lowerCAmelCase							).eval()
				UpperCAmelCase_    			=     model_class(lowerCAmelCase						,		dtype=jnp.floataa							)

				UpperCAmelCase_    			=     convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,		lowerCAmelCase							)
				UpperCAmelCase_    			=     fx_state

				with torch.no_grad():
					UpperCAmelCase_    			=     pt_model(**lowerCAmelCase							).to_tuple()

				UpperCAmelCase_    			=     fx_model(**lowerCAmelCase							).to_tuple()
				self.assertEqual(len(lowerCAmelCase							)						,		len(lowerCAmelCase							)						,		"Output lengths differ between Flax and PyTorch"							)
				for fx_output, pt_output in zip(lowerCAmelCase						,		lowerCAmelCase							):
					self.assert_almost_equals(fx_output[:, -1]						,		pt_output[:, -1].numpy()						,		4e-2							)

				with tempfile.TemporaryDirectory() as tmpdirname:
					pt_model.save_pretrained(lowerCAmelCase							)
					UpperCAmelCase_    			=     model_class.from_pretrained(lowerCAmelCase						,		from_pt=lowerCAmelCase							)

				UpperCAmelCase_    			=     fx_model_loaded(**lowerCAmelCase							).to_tuple()
				self.assertEqual(
				    len(lowerCAmelCase							)						,		len(lowerCAmelCase							)						,		"Output lengths differ between Flax and PyTorch"							)
				for fx_output_loaded, pt_output in zip(lowerCAmelCase						,		lowerCAmelCase							):
					self.assert_almost_equals(fx_output_loaded[:, -1]						,		pt_output[:, -1].numpy()						,		4e-2							)




	@is_pt_flax_cross_test
	def  __A   (						self		:  Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_			,				UpperCAmelCase_    			=     self.model_tester.prepare_config_and_inputs_for_common()
		for model_class in self.all_model_classes:
			with self.subTest(model_class.__name__							):
				# prepare inputs
				UpperCAmelCase_    			=     self._prepare_for_class(lowerCAmelCase						,		lowerCAmelCase							)
				UpperCAmelCase_    			=     {k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

				# load corresponding PyTorch class
				UpperCAmelCase_    			=     model_class.__name__[4:]  # Skip the "Flax" at the beginning
				UpperCAmelCase_    			=     getattr(lowerCAmelCase						,		lowerCAmelCase							)

				UpperCAmelCase_    			=     pt_model_class(lowerCAmelCase							).eval()
				UpperCAmelCase_    			=     model_class(lowerCAmelCase						,		dtype=jnp.floataa							)

				UpperCAmelCase_    			=     load_flax_weights_in_pytorch_model(lowerCAmelCase						,		fx_model.params							)
				UpperCAmelCase_			,				UpperCAmelCase_    			=     pt_inputs["input_ids"].shape
				UpperCAmelCase_    			=     np.random.randint(0						,		seq_length - 1						,		size=(batch_size,)							)
				for batch_idx, start_index in enumerate(lowerCAmelCase							):
					UpperCAmelCase_    			=     0
					UpperCAmelCase_    			=     1
					UpperCAmelCase_    			=     0
					UpperCAmelCase_    			=     1

				# make sure weights are tied in PyTorch
				pt_model.tie_weights()

				with torch.no_grad():
					UpperCAmelCase_    			=     pt_model(**lowerCAmelCase							).to_tuple()

				UpperCAmelCase_    			=     fx_model(**lowerCAmelCase							).to_tuple()
				self.assertEqual(len(lowerCAmelCase							)						,		len(lowerCAmelCase							)						,		"Output lengths differ between Flax and PyTorch"							)
				for fx_output, pt_output in zip(lowerCAmelCase						,		lowerCAmelCase							):
					self.assert_almost_equals(fx_output[:, -1]						,		pt_output[:, -1].numpy()						,		4e-2							)

				with tempfile.TemporaryDirectory() as tmpdirname:
					fx_model.save_pretrained(lowerCAmelCase							)
					UpperCAmelCase_    			=     pt_model_class.from_pretrained(lowerCAmelCase						,		from_flax=lowerCAmelCase							)

				with torch.no_grad():
					UpperCAmelCase_    			=     pt_model_loaded(**lowerCAmelCase							).to_tuple()

				self.assertEqual(
				    len(lowerCAmelCase							)						,		len(lowerCAmelCase							)						,		"Output lengths differ between Flax and PyTorch"							)
				for fx_output, pt_output in zip(lowerCAmelCase						,		lowerCAmelCase							):
					self.assert_almost_equals(fx_output[:, -1]						,		pt_output[:, -1].numpy()						,		4e-2							)




	@slow
	def  __A   (						self		:  Union[str, Any]							):


		'''simple docstring'''







		for model_class_name in self.all_model_classes:
			UpperCAmelCase_    			=     model_class_name.from_pretrained("EleutherAI/gpt-neo-125M"							)
			UpperCAmelCase_    			=     model(np.ones((1, 1)							)							)
			self.assertIsNotNone(lowerCAmelCase							)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_a:      Tuple								=     {
    """configuration_segformer""": ["""SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SegformerConfig""", """SegformerOnnxConfig"""]
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      List[str]								=     ["""SegformerFeatureExtractor"""]
				_a:      Union[str, Any]								=     ["""SegformerImageProcessor"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      Any								=     [
				    """SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """SegformerDecodeHead""",
				    """SegformerForImageClassification""",
				    """SegformerForSemanticSegmentation""",
				    """SegformerLayer""",
				    """SegformerModel""",
				    """SegformerPreTrainedModel""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a:      Optional[Any]								=     [
				    """TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFSegformerDecodeHead""",
				    """TFSegformerForImageClassification""",
				    """TFSegformerForSemanticSegmentation""",
				    """TFSegformerModel""",
				    """TFSegformerPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_segformer import SegformerFeatureExtractor
								from .image_processing_segformer import SegformerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_segformer import (
								    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SegformerDecodeHead,
								    SegformerForImageClassification,
								    SegformerForSemanticSegmentation,
								    SegformerLayer,
								    SegformerModel,
								    SegformerPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_segformer import (
								    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSegformerDecodeHead,
								    TFSegformerForImageClassification,
								    TFSegformerForSemanticSegmentation,
								    TFSegformerModel,
								    TFSegformerPreTrainedModel,
								)

else:
				import sys

				_a:      Optional[int]								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)