





"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
  )



class    __SCREAMING_SNAKE_CASE			:







  '''simple docstring'''


  def __init__(						self      :				Optional[Any]    ,      __a      :				List[str]    ,      __a      :				Tuple=13    ,      __a      :				Dict=7    ,      __a      :				List[str]=True    ,      __a      :				List[str]=True    ,      __a      :				List[str]=True    ,      __a      :				int=True    ,      __a      :				Optional[Any]=99    ,      __a      :				Union[str, Any]=32    ,      __a      :				int=2    ,      __a      :				Tuple=4    ,      __a      :				Dict=37    ,      __a      :				Optional[Any]="gelu"    ,      __a      :				List[Any]=0.1    ,      __a      :				Dict=0.1    ,      __a      :				Tuple=512    ,      __a      :				Union[str, Any]=16    ,      __a      :				List[Any]=2    ,      __a      :				Optional[int]=0.02    ,      __a      :				List[str]=3    ,      __a      :				Optional[Any]=4    ,      __a      :				int=None    ,      )						-> Optional[int]:
        _UpperCamelCase					:		Union[str, Any]  =				parent
        _UpperCamelCase					:		int  =				13
        _UpperCamelCase					:		Optional[int]  =				7
        _UpperCamelCase					:		Tuple  =				True
        _UpperCamelCase					:		List[Any]  =				True
        _UpperCamelCase					:		Optional[int]  =				True
        _UpperCamelCase					:		Union[str, Any]  =				True
        _UpperCamelCase					:		int  =				99
        _UpperCamelCase					:		List[Any]  =				32
        _UpperCamelCase					:		Union[str, Any]  =				2
        _UpperCamelCase					:		Optional[int]  =				4
        _UpperCamelCase					:		List[str]  =				37
        _UpperCamelCase					:		Dict  =				"gelu"
        _UpperCamelCase					:		Any  =				0.1
        _UpperCamelCase					:		Optional[int]  =				0.1
        _UpperCamelCase					:		List[Any]  =				512
        _UpperCamelCase					:		Any  =				16
        _UpperCamelCase					:		Optional[int]  =				False
        _UpperCamelCase					:		Tuple  =				-1
        _UpperCamelCase					:		Optional[int]  =				True
        _UpperCamelCase					:		Optional[int]  =				2
        _UpperCamelCase					:		Any  =				0.02
        _UpperCamelCase					:		int  =				3
        _UpperCamelCase					:		List[str]  =				4
        _UpperCamelCase					:		str  =				None

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> Dict:
        _UpperCamelCase					:		Union[str, Any]  =				ids_tensor([self.batch_size, self.seq_length]    ,      self.vocab_size		)

        _UpperCamelCase					:		Tuple  =				None
        if self.use_input_mask:
              _UpperCamelCase					:		str  =				random_attention_mask([self.batch_size, self.seq_length]		)

        _UpperCamelCase					:		Optional[int]  =				None
        if self.use_token_type_ids:
              _UpperCamelCase					:		List[str]  =				ids_tensor([self.batch_size, self.seq_length]    ,      self.type_vocab_size		)

        _UpperCamelCase					:		Dict  =				None
        _UpperCamelCase					:		int  =				None
        _UpperCamelCase					:		Optional[Any]  =				None
        if self.use_labels:
              _UpperCamelCase					:		List[Any]  =				ids_tensor([self.batch_size]    ,      self.type_sequence_label_size		)
              _UpperCamelCase					:		Any  =				ids_tensor([self.batch_size, self.seq_length]    ,      self.num_labels		)

        _UpperCamelCase					:		str  =				DebertaConfig(
            vocab_size=self.vocab_size    ,      hidden_size=self.hidden_size    ,      num_hidden_layers=self.num_hidden_layers    ,      num_attention_heads=self.num_attention_heads    ,      intermediate_size=self.intermediate_size    ,      hidden_act=self.hidden_act    ,      hidden_dropout_prob=self.hidden_dropout_prob    ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,      max_position_embeddings=self.max_position_embeddings    ,      type_vocab_size=self.type_vocab_size    ,      relative_attention=self.relative_attention    ,      max_relative_positions=self.max_relative_positions    ,      position_biased_input=self.position_biased_input    ,      initializer_range=self.initializer_range    ,      return_dict=__a    ,      )

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

  def       __SCREAMING_SNAKE_CASE (						self      :				int    ,      __a      :				Any    ,      __a      :				Optional[Any]    ,      __a      :				List[Any]    ,      __a      :				Optional[Any]    ,      __a      :				int    ,      __a      :				Optional[Any]    ,      __a      :				Any		)						-> Tuple:
        _UpperCamelCase					:		str  =				TFDebertaModel(config=__a		)
        _UpperCamelCase					:		Union[str, Any]  =				{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

        _UpperCamelCase					:		int  =				[input_ids, input_mask]
        _UpperCamelCase					:		Tuple  =				model(__a		)

        _UpperCamelCase					:		List[Any]  =				model(__a		)

        self.parent.assertEqual(result.last_hidden_state.shape    ,      (self.batch_size, self.seq_length, self.hidden_size)		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]    ,      __a      :				List[str]    ,      __a      :				Union[str, Any]    ,      __a      :				List[Any]    ,      __a      :				Tuple    ,      __a      :				List[str]    ,      __a      :				List[Any]    ,      __a      :				str		)						-> Dict:
        _UpperCamelCase					:		Optional[Any]  =				TFDebertaForMaskedLM(config=__a		)
        _UpperCamelCase					:		Optional[int]  =				{
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }
        _UpperCamelCase					:		str  =				model(__a		)
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.seq_length, self.vocab_size)		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]    ,      __a      :				List[str]    ,      __a      :				List[str]    ,      __a      :				Union[str, Any]    ,      __a      :				int    ,      __a      :				int    ,      __a      :				List[str]    ,      __a      :				Dict		)						-> Optional[int]:
        _UpperCamelCase					:		Any  =				self.num_labels
        _UpperCamelCase					:		List[str]  =				TFDebertaForSequenceClassification(config=__a		)
        _UpperCamelCase					:		List[Any]  =				{
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }

        _UpperCamelCase					:		List[str]  =				model(__a		)
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.num_labels)		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]    ,      __a      :				Any    ,      __a      :				Dict    ,      __a      :				int    ,      __a      :				Tuple    ,      __a      :				Union[str, Any]    ,      __a      :				Optional[Any]    ,      __a      :				int		)						-> Tuple:
        _UpperCamelCase					:		Optional[Any]  =				self.num_labels
        _UpperCamelCase					:		Union[str, Any]  =				TFDebertaForTokenClassification(config=__a		)
        _UpperCamelCase					:		Dict  =				{
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }
        _UpperCamelCase					:		List[str]  =				model(__a		)
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.seq_length, self.num_labels)		)

  def       __SCREAMING_SNAKE_CASE (						self      :				str    ,      __a      :				int    ,      __a      :				List[str]    ,      __a      :				Union[str, Any]    ,      __a      :				Optional[int]    ,      __a      :				Tuple    ,      __a      :				Optional[Any]    ,      __a      :				Union[str, Any]		)						-> List[str]:
        _UpperCamelCase					:		Dict  =				TFDebertaForQuestionAnswering(config=__a		)
        _UpperCamelCase					:		int  =				{
            "input_ids": input_ids,
            "attention_mask": input_mask,
            "token_type_ids": token_type_ids,
        }

        _UpperCamelCase					:		List[Any]  =				model(__a		)
        self.parent.assertEqual(result.start_logits.shape    ,      (self.batch_size, self.seq_length)		)
        self.parent.assertEqual(result.end_logits.shape    ,      (self.batch_size, self.seq_length)		)


  def       __SCREAMING_SNAKE_CASE (						self      :				Tuple		)						-> Tuple:
        _UpperCamelCase					:		List[Any]  =				self.prepare_config_and_inputs()
        (
            (
            _UpperCamelCase
        ),     (
            _UpperCamelCase
        ),     (
            _UpperCamelCase
        ),     (
            _UpperCamelCase
        ),     (
            _UpperCamelCase
        ),     (
            _UpperCamelCase
        ),     (
            _UpperCamelCase
        ),     
        )					:		Dict  =				config_and_inputs
        _UpperCamelCase					:		Any  =				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
        return config, inputs_dict





@require_tf
class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase						,     _UpperCamelCase						,     unittest.TestCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:List[str]		   =    (
      (
          TFDebertaModel,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  SCREAMING_SNAKE_CASE__							:int		   =    (
      {
          "feature-extraction": TFDebertaModel,
          "fill-mask": TFDebertaForMaskedLM,
          "question-answering": TFDebertaForQuestionAnswering,
          "text-classification": TFDebertaForSequenceClassification,
          "token-classification": TFDebertaForTokenClassification,
          "zero-shot": TFDebertaForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )

  SCREAMING_SNAKE_CASE__							:Optional[Any]		   =    False
  SCREAMING_SNAKE_CASE__							:Union[str, Any]		   =    False

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]		)						-> List[str]:
        _UpperCamelCase					:		List[Any]  =				TFDebertaModelTester(self		)
        _UpperCamelCase					:		Optional[Any]  =				ConfigTester(self    ,      config_class=__a    ,      hidden_size=37		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]		)						-> Tuple:
        self.config_tester.run_common_tests()

  def       __SCREAMING_SNAKE_CASE (						self      :				List[Any]		)						-> List[str]:
        _UpperCamelCase					:		Optional[int]  =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]		)						-> Union[str, Any]:
        _UpperCamelCase					:		str  =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Any		)						-> int:
        _UpperCamelCase					:		Dict  =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> List[str]:
        _UpperCamelCase					:		Optional[int]  =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Tuple		)						-> Any:
        _UpperCamelCase					:		Optional[int]  =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__a		)


  @slow
  def       __SCREAMING_SNAKE_CASE (						self      :				Dict		)						-> List[str]:
        _UpperCamelCase					:		Tuple  =				TFDebertaModel.from_pretrained("kamalkraj/deberta-base"		)
        self.assertIsNotNone(__a		)





@require_tf
class    __SCREAMING_SNAKE_CASE			(  unittest.TestCase      ):







  '''simple docstring'''


  @unittest.skip(reason="Model not available yet"		)
  def       __SCREAMING_SNAKE_CASE (						self      :				List[Any]		)						-> Dict:
        pass


  @slow
  def       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> str:
        _UpperCamelCase					:		Tuple  =				TFDebertaModel.from_pretrained("kamalkraj/deberta-base"		)
        _UpperCamelCase					:		Optional[Any]  =				tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]		)
        _UpperCamelCase					:		Optional[Any]  =				tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
        _UpperCamelCase					:		List[Any]  =				model(__a    ,      attention_mask=__a		)[0]

        _UpperCamelCase					:		str  =				tf.constant(
            [
                [
                    [-0.59_85_58_96, -0.80_55_25_66, -0.8_46_21_35],
                    [1.4_48_40_25, -0.93_48_37_94, -0.80_59_30_85],
                    [0.3_12_27_41, 0.00_31_60_59, -1.4_13_13_77],
                ]
            ]		)
        tf.debugging.assert_near(output[:, 1:4, 1:4]    ,      __a    ,      atol=1e-4		)







"""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    __SCREAMING_SNAKE_CASE			:







  '''simple docstring'''


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

  def       __SCREAMING_SNAKE_CASE (						self      :				List[Any]		)						-> Union[str, Any]:
        _UpperCamelCase					:		int  =				ids_tensor([self.batch_size, self.seq_length]    ,      self.vocab_size		)

        _UpperCamelCase					:		List[Any]  =				None
        if self.use_input_mask:
              _UpperCamelCase					:		int  =				random_attention_mask([self.batch_size, self.seq_length]		)

        _UpperCamelCase					:		Optional[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       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> List[Any]:
        _UpperCamelCase					:		Optional[Any]  =				self.prepare_config_and_inputs()
        _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		Union[str, Any]  =				config_and_inputs
        _UpperCamelCase					:		Optional[int]  =				{"input_ids": input_ids, "attention_mask": attention_mask}
        return config, inputs_dict

  def       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> List[Any]:
        _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		int  =				self.prepare_config_and_inputs()

        _UpperCamelCase					:		Tuple  =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
        _UpperCamelCase					:		Optional[int]  =				ids_tensor([self.batch_size, self.seq_length]    ,      vocab_size=2		)

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

  def       __SCREAMING_SNAKE_CASE (						self      :				Tuple    ,      __a      :				Any    ,      __a      :				int    ,      __a      :				List[str]    ,      __a      :				List[Any]		)						-> List[Any]:
        _UpperCamelCase					:		List[str]  =				20
        _UpperCamelCase					:		Tuple  =				model_class_name(__a		)

        _UpperCamelCase					:		Optional[Any]  =				model.init_cache(input_ids.shape[0]    ,      __a		)
        _UpperCamelCase					:		Union[str, Any]  =				jnp.ones((input_ids.shape[0], max_decoder_length)    ,      dtype="i4"		)

        _UpperCamelCase					:		Optional[Any]  =				jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1		)[None, :]    ,      (input_ids.shape[0], input_ids.shape[-1] - 1)		)
        _UpperCamelCase					:		Optional[int]  =				model(
            input_ids[:, :-1]    ,      attention_mask=__a    ,      past_key_values=__a    ,      position_ids=__a    ,      )

        _UpperCamelCase					:		Dict  =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,      dtype="i4"		)
        _UpperCamelCase					:		Optional[Any]  =				model(
            input_ids[:, -1:]    ,      attention_mask=__a    ,      past_key_values=outputs_cache.past_key_values    ,      position_ids=__a    ,      )

        _UpperCamelCase					:		Optional[Any]  =				model(__a		)

        _UpperCamelCase					:		Optional[int]  =				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       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]    ,      __a      :				Dict    ,      __a      :				str    ,      __a      :				Any    ,      __a      :				Dict		)						-> int:
        _UpperCamelCase					:		Optional[Any]  =				20
        _UpperCamelCase					:		Tuple  =				model_class_name(__a		)

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

        _UpperCamelCase					:		str  =				model.init_cache(input_ids.shape[0]    ,      __a		)
        _UpperCamelCase					:		str  =				jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1		)[None, :]    ,      (input_ids.shape[0], input_ids.shape[-1] - 1)		)

        _UpperCamelCase					:		Tuple  =				model(
            input_ids[:, :-1]    ,      attention_mask=__a    ,      past_key_values=__a    ,      position_ids=__a    ,      )
        _UpperCamelCase					:		Any  =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,      dtype="i4"		)
        _UpperCamelCase					:		List[Any]  =				model(
            input_ids[:, -1:]    ,      past_key_values=outputs_cache.past_key_values    ,      attention_mask=__a    ,      position_ids=__a    ,      )

        _UpperCamelCase					:		Tuple  =				model(__a    ,      attention_mask=__a		)

        _UpperCamelCase					:		int  =				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    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase						,     _UpperCamelCase						,     unittest.TestCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Tuple		   =    (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
  SCREAMING_SNAKE_CASE__							:List[str]		   =    (FlaxGPTaLMHeadModel,) if is_flax_available() else ()

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]		)						-> Optional[int]:
        _UpperCamelCase					:		str  =				FlaxGPTaModelTester(self		)

  def       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> Optional[Any]:
        for model_class_name in self.all_model_classes:
              _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		List[str]  =				self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(__a    ,      __a    ,      __a    ,      __a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				int		)						-> int:
        for model_class_name in self.all_model_classes:
              _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		int  =				self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward_with_attn_mask(
                  __a    ,      __a    ,      __a    ,      __a		)

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

        _UpperCamelCase					:		Tuple  =				FlaxGPTaLMHeadModel.from_pretrained("gpt2"		)
        _UpperCamelCase					:		Optional[Any]  =				False
        _UpperCamelCase					:		str  =				model.config.eos_token_id

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

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

        _UpperCamelCase					:		Tuple  =				tokenizer.batch_decode(__a    ,      skip_special_tokens=__a		)

        _UpperCamelCase					:		Any  =				[
            "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       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]		)						-> List[str]:
        _UpperCamelCase,     _UpperCamelCase					:		List[Any]  =				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					:		Optional[Any]  =				self._prepare_for_class(__a    ,      __a		)
                    _UpperCamelCase					:		int  =				{k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

                    # load corresponding PyTorch class
                    _UpperCamelCase					:		str  =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
                    _UpperCamelCase					:		Union[str, Any]  =				getattr(__a    ,      __a		)

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

                    _UpperCamelCase					:		int  =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,      __a		)
                    _UpperCamelCase					:		List[Any]  =				fx_state

                    with torch.no_grad():
                          _UpperCamelCase					:		List[str]  =				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					:		Any  =				model_class.from_pretrained(__a    ,      from_pt=__a		)

                    _UpperCamelCase					:		Tuple  =				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       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]		)						-> Dict:
        _UpperCamelCase,     _UpperCamelCase					:		Optional[Any]  =				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					:		Union[str, Any]  =				self._prepare_for_class(__a    ,      __a		)
                    _UpperCamelCase					:		Optional[Any]  =				{k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

                    # load corresponding PyTorch class
                    _UpperCamelCase					:		Any  =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
                    _UpperCamelCase					:		Union[str, Any]  =				getattr(__a    ,      __a		)

                    _UpperCamelCase					:		Dict  =				pt_model_class(__a		).eval()
                    _UpperCamelCase					:		Tuple  =				model_class(__a    ,      dtype=jnp.floataa		)

                    _UpperCamelCase					:		Dict  =				load_flax_weights_in_pytorch_model(__a    ,      fx_model.params		)
                    _UpperCamelCase,     _UpperCamelCase					:		int  =				pt_inputs["input_ids"].shape
                    _UpperCamelCase					:		Optional[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					:		Dict  =				0
                          _UpperCamelCase					:		Optional[int]  =				1

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

                    with torch.no_grad():
                          _UpperCamelCase					:		Optional[int]  =				pt_model(**__a		).to_tuple()

                    _UpperCamelCase					:		List[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					:		Tuple  =				pt_model_class.from_pretrained(__a    ,      from_flax=__a		)

                    with torch.no_grad():
                          _UpperCamelCase					:		int  =				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       __SCREAMING_SNAKE_CASE (						self      :				List[Any]		)						-> int:
        for model_class_name in self.all_model_classes:
              _UpperCamelCase					:		Union[str, Any]  =				model_class_name.from_pretrained("gpt2"    ,      from_pt=__a		)
              _UpperCamelCase					:		List[str]  =				model(np.ones((1, 1)		)		)
              self.assertIsNotNone(__a		)
