'''simple docstring'''




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, 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, floats_tensor, 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.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
  import torch

class      lowerCamelCase_     :
      '''simple docstring'''

      def __init__(	self						:    Any						, A						:    Optional[int]						, A						:    List[Any]=14						, A						:    int=7						, A						:    Dict=True						, A						:    Optional[Any]=True						, A						:    Dict=False						, A						:    Tuple=True						, A						:    Union[str, Any]=99						, A						:    List[str]=32						, A						:    Any=5						, A						:    Any=4						, A						:    Any=37						, A						:    Optional[Any]="gelu"						, A						:    Tuple=0.1						, A						:    Dict=0.1						, A						:    Union[str, Any]=512						, A						:    str=0.02						, ):
             _UpperCAmelCase					:  str											=   parent
             _UpperCAmelCase					:  str											=   batch_size
             _UpperCAmelCase					:  Any											=   seq_length
             _UpperCAmelCase					:  List[str]											=   is_training
             _UpperCAmelCase					:  Any											=   use_input_mask
             _UpperCAmelCase					:  int											=   use_token_type_ids
             _UpperCAmelCase					:  Dict											=   use_labels
             _UpperCAmelCase					:  str											=   vocab_size
             _UpperCAmelCase					:  Tuple											=   hidden_size
             _UpperCAmelCase					:  Union[str, Any]											=   num_hidden_layers
             _UpperCAmelCase					:  int											=   num_attention_heads
             _UpperCAmelCase					:  List[str]											=   intermediate_size
             _UpperCAmelCase					:  int											=   hidden_act
             _UpperCAmelCase					:  str											=   hidden_dropout_prob
             _UpperCAmelCase					:  List[Any]											=   attention_probs_dropout_prob
             _UpperCAmelCase					:  str											=   max_position_embeddings
             _UpperCAmelCase					:  Union[str, Any]											=   initializer_range
             _UpperCAmelCase					:  str											=   None
             _UpperCAmelCase					:  Any											=   vocab_size - 1
             _UpperCAmelCase					:  Optional[Any]											=   vocab_size - 1
             _UpperCAmelCase					:  Union[str, Any]											=   vocab_size - 1




      def 					_A     (	self						:    Optional[int]	):
             _UpperCAmelCase					:  Dict											=   ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size	)

             _UpperCAmelCase					:  Tuple											=   None
             if self.use_input_mask:
                    _UpperCAmelCase					:  Optional[int]											=   random_attention_mask([self.batch_size, self.seq_length]	)

             _UpperCAmelCase					:  List[Any]											=   GPTaConfig(
                 vocab_size=self.vocab_size						, n_embd=self.hidden_size						, n_layer=self.num_hidden_layers						, n_head=self.num_attention_heads						, n_positions=self.max_position_embeddings						, use_cache=A						, bos_token_id=self.bos_token_id						, eos_token_id=self.eos_token_id						, pad_token_id=self.pad_token_id						, )

             return (config, input_ids, input_mask)




      def 					_A     (	self						:    Tuple	):
             _UpperCAmelCase					:  Optional[Any]											=   self.prepare_config_and_inputs()
             _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase					:  List[Any]											=   config_and_inputs
             _UpperCAmelCase					:  Optional[int]											=   {"input_ids": input_ids, "attention_mask": attention_mask}
             return config, inputs_dict




      def 					_A     (	self						:    List[Any]	):
             _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase					:  Union[str, Any]											=   self.prepare_config_and_inputs()

             _UpperCAmelCase					:  List[Any]											=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
             _UpperCAmelCase					:  List[Any]											=   ids_tensor([self.batch_size, self.seq_length]						, vocab_size=2	)

             return (
                 config,
                 input_ids,
                 attention_mask,
                 encoder_hidden_states,
                 encoder_attention_mask,
             )




      def 					_A     (	self						:    Optional[int]						, A						:    List[str]						, A						:    Any						, A						:    Dict						, A						:    str	):
             _UpperCAmelCase					:  Optional[Any]											=   20
             _UpperCAmelCase					:  Optional[int]											=   model_class_name(A	)

             _UpperCAmelCase					:  str											=   model.init_cache(input_ids.shape[0]						, A	)
             _UpperCAmelCase					:  int											=   jnp.ones((input_ids.shape[0], max_decoder_length)						, dtype="i4"	)

             _UpperCAmelCase					:  List[str]											=   jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1	)[None, :]						, (input_ids.shape[0], input_ids.shape[-1] - 1)	)
             _UpperCAmelCase					:  List[str]											=   model(
                 input_ids[:, :-1]						, attention_mask=A						, past_key_values=A						, position_ids=A						, )

             _UpperCAmelCase					:  Tuple											=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						, dtype="i4"	)
             _UpperCAmelCase					:  Union[str, Any]											=   model(
                 input_ids[:, -1:]						, attention_mask=A						, past_key_values=outputs_cache.past_key_values						, position_ids=A						, )

             _UpperCAmelCase					:  Optional[int]											=   model(A	)

             _UpperCAmelCase					:  Dict											=   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						:    Optional[Any]						, A						:    Tuple						, A						:    Any						, A						:    List[Any]						, A						:    Any	):
             _UpperCAmelCase					:  Dict											=   20
             _UpperCAmelCase					:  int											=   model_class_name(A	)

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

             _UpperCAmelCase					:  List[str]											=   model.init_cache(input_ids.shape[0]						, A	)
             _UpperCAmelCase					:  Tuple											=   jnp.broadcast_to(
                 jnp.arange(input_ids.shape[-1] - 1	)[None, :]						, (input_ids.shape[0], input_ids.shape[-1] - 1)	)

             _UpperCAmelCase					:  List[str]											=   model(
                 input_ids[:, :-1]						, attention_mask=A						, past_key_values=A						, position_ids=A						, )
             _UpperCAmelCase					:  Union[str, Any]											=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						, dtype="i4"	)
             _UpperCAmelCase					:  Optional[Any]											=   model(
                 input_ids[:, -1:]						, past_key_values=outputs_cache.past_key_values						, attention_mask=A						, position_ids=A						, )

             _UpperCAmelCase					:  str											=   model(A						, attention_mask=A	)

             _UpperCAmelCase					:  List[str]											=   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      lowerCamelCase_     (snake_case__							,			snake_case__							,			unittest.TestCase   ):
      '''simple docstring'''

      __UpperCamelCase:  int      =						(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
      __UpperCamelCase:  Dict      =						(FlaxGPTaLMHeadModel,) if is_flax_available() else ()




      def 					_A     (	self						:    Optional[int]	):
             _UpperCAmelCase					:  Any											=   FlaxGPTaModelTester(self	)




      def 					_A     (	self						:    Any	):
             for model_class_name in self.all_model_classes:
                    _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase					:  Any											=   self.model_tester.prepare_config_and_inputs()
                    self.model_tester.check_use_cache_forward(A						, A						, A						, A	)




      def 					_A     (	self						:    Optional[Any]	):
             for model_class_name in self.all_model_classes:
                    _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase					:  str											=   self.model_tester.prepare_config_and_inputs()
                    self.model_tester.check_use_cache_forward_with_attn_mask(
                        A						, A						, A						, A	)




      @slow
      def 					_A     (	self						:    Optional[int]	):
             _UpperCAmelCase					:  List[Any]											=   GPTaTokenizer.from_pretrained("gpt2"						, pad_token="</s>"						, padding_side="left"	)
             _UpperCAmelCase					:  Optional[Any]											=   tokenizer(["Hello this is a long string", "Hey"]						, return_tensors="np"						, padding=A						, truncation=A	)

             _UpperCAmelCase					:  Any											=   FlaxGPTaLMHeadModel.from_pretrained("gpt2"	)
             _UpperCAmelCase					:  str											=   False
             _UpperCAmelCase					:  str											=   model.config.eos_token_id

             _UpperCAmelCase					:  Any											=   jax.jit(model.generate	)

             _UpperCAmelCase					:  Any											=   jit_generate(inputs["input_ids"]						, attention_mask=inputs["attention_mask"]	).sequences

             _UpperCAmelCase					:  List[Any]											=   tokenizer.batch_decode(A						, skip_special_tokens=A	)

             _UpperCAmelCase					:  str											=   [
                 "Hello this is a long string of words. I'm going to start with the first one.\n",
                 "Hey, I'm not sure if I'm going to be able to do",
             ]

             self.assertListEqual(A						, A	)




      @is_pt_flax_cross_test
      def 					_A     (	self						:    Optional[int]	):
             _UpperCAmelCase				,  _UpperCAmelCase					:  int											=   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					:  Dict											=   self._prepare_for_class(A						, A	)
                           _UpperCAmelCase					:  Tuple											=   {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

                           # load corresponding PyTorch class
                           _UpperCAmelCase					:  Optional[Any]											=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
                           _UpperCAmelCase					:  List[str]											=   getattr(A						, A	)

                           _UpperCAmelCase				,  _UpperCAmelCase					:  Union[str, Any]											=   pt_inputs["input_ids"].shape
                           _UpperCAmelCase					:  Union[str, Any]											=   np.random.randint(0						, seq_length - 1						, size=(batch_size,)	)
                           for batch_idx, start_index in enumerate(A	):
                                  _UpperCAmelCase					:  Dict											=   0
                                  _UpperCAmelCase					:  Union[str, Any]											=   1
                                  _UpperCAmelCase					:  List[Any]											=   0
                                  _UpperCAmelCase					:  int											=   1
                           _UpperCAmelCase					:  Tuple											=   pt_model_class(A	).eval()
                           _UpperCAmelCase					:  str											=   model_class(A						, dtype=jnp.floataa	)

                           _UpperCAmelCase					:  Any											=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()						, A	)
                           _UpperCAmelCase					:  List[Any]											=   fx_state

                           with torch.no_grad():
                                  _UpperCAmelCase					:  Optional[int]											=   pt_model(**A	).to_tuple()

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

                           with tempfile.TemporaryDirectory() as tmpdirname:
                                  pt_model.save_pretrained(A	)
                                  _UpperCAmelCase					:  List[Any]											=   model_class.from_pretrained(A						, from_pt=A	)

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




      @is_pt_flax_cross_test
      def 					_A     (	self						:    int	):
             _UpperCAmelCase				,  _UpperCAmelCase					:  Dict											=   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					:  Tuple											=   self._prepare_for_class(A						, A	)
                           _UpperCAmelCase					:  Dict											=   {k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

                           # load corresponding PyTorch class
                           _UpperCAmelCase					:  List[Any]											=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
                           _UpperCAmelCase					:  List[Any]											=   getattr(A						, A	)

                           _UpperCAmelCase					:  List[Any]											=   pt_model_class(A	).eval()
                           _UpperCAmelCase					:  Tuple											=   model_class(A						, dtype=jnp.floataa	)

                           _UpperCAmelCase					:  Tuple											=   load_flax_weights_in_pytorch_model(A						, fx_model.params	)
                           _UpperCAmelCase				,  _UpperCAmelCase					:  List[Any]											=   pt_inputs["input_ids"].shape
                           _UpperCAmelCase					:  int											=   np.random.randint(0						, seq_length - 1						, size=(batch_size,)	)
                           for batch_idx, start_index in enumerate(A	):
                                  _UpperCAmelCase					:  List[str]											=   0
                                  _UpperCAmelCase					:  Optional[Any]											=   1
                                  _UpperCAmelCase					:  str											=   0
                                  _UpperCAmelCase					:  Optional[Any]											=   1

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

                           with torch.no_grad():
                                  _UpperCAmelCase					:  Dict											=   pt_model(**A	).to_tuple()

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

                           with tempfile.TemporaryDirectory() as tmpdirname:
                                  fx_model.save_pretrained(A	)
                                  _UpperCAmelCase					:  Union[str, Any]											=   pt_model_class.from_pretrained(A						, from_flax=A	)

                           with torch.no_grad():
                                  _UpperCAmelCase					:  List[str]											=   pt_model_loaded(**A	).to_tuple()

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





      @slow
      def 					_A     (	self						:    Union[str, Any]	):
             for model_class_name in self.all_model_classes:
                    _UpperCAmelCase					:  List[str]											=   model_class_name.from_pretrained("gpt2"						, from_pt=A	)
                    _UpperCAmelCase					:  Any											=   model(np.ones((1, 1)	)	)
                    self.assertIsNotNone(A	)


'''simple docstring'''




import warnings

from .generation import FlaxGenerationMixin

class      lowerCamelCase_     (snake_case__   ):
      '''simple docstring'''

      warnings.warn(
          "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
          "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."							,			snake_case__							,			)

