




'''simple docstring'''


import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
  import torch





class  __lowercase    :







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







   def 		UpperCamelCase__      (						self   )					->     Any:
     __a								=       ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size   )

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

     __a								=       GPTJConfig(
         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   ,				rotary_dim=self.rotary_dim   ,				)

     return (config, input_ids, input_mask)







   def 		UpperCamelCase__      (						self   )					->     Dict:
     __a								=       self.prepare_config_and_inputs()
     __a						,							__a						,							__a								=       config_and_inputs
     __a								=       {'input_ids': input_ids, 'attention_mask': attention_mask}
     return config, inputs_dict







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     List[Any]:
     __a								=       20
     __a								=       model_class_name(A_   )

     __a								=       model.init_cache(input_ids.shape[0]   ,				A_   )
     __a								=       jnp.ones((input_ids.shape[0], max_decoder_length)   ,				dtype='i4'   )

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

     __a								=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,				dtype='i4'   )
     __a								=       model(
         input_ids[:, -1:]   ,				attention_mask=A_   ,				past_key_values=outputs_cache.past_key_values   ,				position_ids=A_   ,				)

     __a								=       model(A_   )

     __a								=       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 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Optional[int]:
     __a								=       20
     __a								=       model_class_name(A_   )

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

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

     __a								=       model(
         input_ids[:, :-1]   ,				attention_mask=A_   ,				past_key_values=A_   ,				position_ids=A_   ,				)
     __a								=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,				dtype='i4'   )
     __a								=       model(
         input_ids[:, -1:]   ,				past_key_values=outputs_cache.past_key_values   ,				attention_mask=A_   ,				position_ids=A_   ,				)

     __a								=       model(A_   ,				attention_mask=A_   )

     __a								=       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  __lowercase    (	_lowercase							,				_lowercase							,				unittest.TestCase			):
   _a    						=   (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
   _a    						=   (FlaxGPTJForCausalLM,) if is_flax_available() else ()







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       FlaxGPTJModelTester(self   )







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     for model_class_name in self.all_model_classes:
       __a						,							__a						,							__a								=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward(A_   ,				A_   ,				A_   ,				A_   )







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     for model_class_name in self.all_model_classes:
       __a						,							__a						,							__a								=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward_with_attn_mask(
           A_   ,				A_   ,				A_   ,				A_   )







   @tooslow
   def 		UpperCamelCase__      (						self   )					->     Optional[int]:
     __a								=       GPTaTokenizer.from_pretrained('gpt2'   ,				pad_token='<|endoftext|>'   ,				padding_side='left'   )
     __a								=       tokenizer(['Hello this is a long string', 'Hey']   ,				return_tensors='np'   ,				padding=A_   ,				truncation=A_   )

     __a								=       FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'   )
     __a								=       False
     __a								=       model.config.eos_token_id

     __a								=       jax.jit(model.generate   )

     __a								=       jit_generate(
         inputs['input_ids']   ,				attention_mask=inputs['attention_mask']   ,				pad_token_id=tokenizer.pad_token_id   ).sequences

     __a								=       tokenizer.batch_decode(A_   ,				skip_special_tokens=A_   )

     __a								=       [
         '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(A_   ,				A_   )







   @is_pt_flax_cross_test
   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a						,							__a								=       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
         __a								=       self._prepare_for_class(A_   ,				A_   )
         __a								=       {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

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

         __a								=       convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,				A_   )
         __a								=       fx_state

         with torch.no_grad():
           __a								=       pt_model(**A_   ).to_tuple()

         __a								=       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_   )
           __a								=       model_class.from_pretrained(A_   ,				from_pt=A_   )

         __a								=       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 		UpperCamelCase__      (						self   )					->     Any:
     __a						,							__a								=       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
         __a								=       self._prepare_for_class(A_   ,				A_   )
         __a								=       {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

         __a								=       pt_model_class(A_   ).eval()
         __a								=       model_class(A_   ,				dtype=jnp.floataa   )

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

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

         with torch.no_grad():
           __a								=       pt_model(**A_   ).to_tuple()

         __a								=       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_   )
           __a								=       pt_model_class.from_pretrained(A_   ,				from_flax=A_   )

         with torch.no_grad():
           __a								=       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   )







   @tooslow
   def 		UpperCamelCase__      (						self   )					->     Dict:
     for model_class_name in self.all_model_classes:
       __a								=       model_class_name.from_pretrained('EleutherAI/gpt-j-6B'   )
       __a								=       model(np.ones((1, 1)   )   )
       self.assertIsNotNone(A_   )





"""simple docstring"""


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase				 =					{
    '''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
    '''feature_extraction_mctct''': ['''MCTCTFeatureExtractor'''],
    '''processing_mctct''': ['''MCTCTProcessor'''],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MCTCTForCTC''',
		    '''MCTCTModel''',
		    '''MCTCTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
		from .feature_extraction_mctct import MCTCTFeatureExtractor
		from .processing_mctct import MCTCTProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
		import sys

		_lowercase				 =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)