
"""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 							__magic_name__     :





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





     def 							lowerCAmelCase      (				self   :							Dict    ):
            __snake_case     			=       ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size    )

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

            __snake_case     			=       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=lowercase_							,						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 							lowerCAmelCase      (				self   :							int    ):
            __snake_case     			=       self.prepare_config_and_inputs()
            __snake_case     			=       config_and_inputs
            __snake_case     			=       {"""input_ids""": input_ids, """attention_mask""": attention_mask}
            return config, inputs_dict





     def 							lowerCAmelCase      (				self   :							List[Any]							,						snake_case_   :							Tuple							,						snake_case_   :							int							,						snake_case_   :							Tuple							,						snake_case_   :							Optional[int]    ):
            __snake_case     			=       20
            __snake_case     			=       model_class_name(lowercase_    )

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

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

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

            __snake_case     			=       model(lowercase_    )

            __snake_case     			=       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 							lowerCAmelCase      (				self   :							List[Any]							,						snake_case_   :							str							,						snake_case_   :							Optional[Any]							,						snake_case_   :							List[str]							,						snake_case_   :							Optional[Any]    ):
            __snake_case     			=       20
            __snake_case     			=       model_class_name(lowercase_    )

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

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

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

            __snake_case     			=       model(lowercase_							,						attention_mask=lowercase_    )

            __snake_case     			=       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 							__magic_name__     (		_UpperCAmelCase      ,		_UpperCAmelCase      ,		unittest.TestCase   ):
     _SCREAMING_SNAKE_CASE       :					List[Any]     							=   (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
     _SCREAMING_SNAKE_CASE       :					List[Any]     							=   (FlaxGPTJForCausalLM,) if is_flax_available() else ()





     def 							lowerCAmelCase      (				self   :							List[str]    ):
            __snake_case     			=       FlaxGPTJModelTester(self    )





     def 							lowerCAmelCase      (				self   :							Optional[int]    ):
            for model_class_name in self.all_model_classes:
                   __snake_case     			=       self.model_tester.prepare_config_and_inputs()
                   self.model_tester.check_use_cache_forward(lowercase_							,						lowercase_							,						lowercase_							,						lowercase_    )





     def 							lowerCAmelCase      (				self   :							Dict    ):
            for model_class_name in self.all_model_classes:
                   __snake_case     			=       self.model_tester.prepare_config_and_inputs()
                   self.model_tester.check_use_cache_forward_with_attn_mask(
                       lowercase_							,						lowercase_							,						lowercase_							,						lowercase_    )





     @tooslow
     def 							lowerCAmelCase      (				self   :							List[Any]    ):
            __snake_case     			=       GPTaTokenizer.from_pretrained("gpt2"							,						pad_token="<|endoftext|>"							,						padding_side="left"    )
            __snake_case     			=       tokenizer(["Hello this is a long string", "Hey"]							,						return_tensors="np"							,						padding=lowercase_							,						truncation=lowercase_    )

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

            __snake_case     			=       jax.jit(model.generate    )

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

            __snake_case     			=       tokenizer.batch_decode(lowercase_							,						skip_special_tokens=lowercase_    )

            __snake_case     			=       [
                """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(lowercase_							,						lowercase_    )





     @is_pt_flax_cross_test
     def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
            __snake_case     			=       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
                          __snake_case     			=       self._prepare_for_class(lowercase_							,						lowercase_    )
                          __snake_case     			=       {k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

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

                          __snake_case     			=       convert_pytorch_state_dict_to_flax(pt_model.state_dict()							,						lowercase_    )
                          __snake_case     			=       fx_state

                          with torch.no_grad():
                                 __snake_case     			=       pt_model(**lowercase_    ).to_tuple()

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

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

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





     @is_pt_flax_cross_test
     def 							lowerCAmelCase      (				self   :							List[Any]    ):
            __snake_case     			=       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
                          __snake_case     			=       self._prepare_for_class(lowercase_							,						lowercase_    )
                          __snake_case     			=       {k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

                          __snake_case     			=       pt_model_class(lowercase_    ).eval()
                          __snake_case     			=       model_class(lowercase_							,						dtype=jnp.floataa    )

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

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

                          with torch.no_grad():
                                 __snake_case     			=       pt_model(**lowercase_    ).to_tuple()

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

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

                          with torch.no_grad():
                                 __snake_case     			=       pt_model_loaded(**lowercase_    ).to_tuple()

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





     @tooslow
     def 							lowerCAmelCase      (				self   :							int    ):
            for model_class_name in self.all_model_classes:
                   __snake_case     			=       model_class_name.from_pretrained("EleutherAI/gpt-j-6B"    )
                   __snake_case     			=       model(np.ones((1, 1)    )    )
                   self.assertIsNotNone(lowercase_    )


"""simple docstring"""







import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     List[Any]:







							"""simple docstring"""


							__snake_case     			=       TapasConfig.from_json_file(SCREAMING_SNAKE_CASE )
							# set absolute/relative position embeddings parameter
							__snake_case     			=       reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														__snake_case     			=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE )
							elif task == "WTQ":
														# run_task_main.py hparams
														__snake_case     			=       4
														__snake_case     			=       True
														# hparam_utils.py hparams
														__snake_case     			=       0.664_694
														__snake_case     			=       0.207_951
														__snake_case     			=       0.121_194
														__snake_case     			=       True
														__snake_case     			=       True
														__snake_case     			=       False
														__snake_case     			=       0.0_352_513

														__snake_case     			=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE )
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														__snake_case     			=       4
														__snake_case     			=       False
														# hparam_utils.py hparams
														__snake_case     			=       36.4_519
														__snake_case     			=       0.903_421
														__snake_case     			=       222.088
														__snake_case     			=       True
														__snake_case     			=       True
														__snake_case     			=       True
														__snake_case     			=       0.763_141

														__snake_case     			=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE )
							elif task == "TABFACT":
														__snake_case     			=       TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE )
							elif task == "MLM":
														__snake_case     			=       TapasForMaskedLM(config=SCREAMING_SNAKE_CASE )
							elif task == "INTERMEDIATE_PRETRAINING":
														__snake_case     			=       TapasModel(config=SCREAMING_SNAKE_CASE )
							else:
														raise ValueError(F'''Task {task} not supported.''' )

							print(F'''Building PyTorch model from configuration: {config}''' )
							# Load weights from tf checkpoint
							load_tf_weights_in_tapas(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

							# Save pytorch-model (weights and configuration)
							print(F'''Save PyTorch model to {pytorch_dump_path}''' )
							model.save_pretrained(SCREAMING_SNAKE_CASE )

							# Save tokenizer files
							print(F'''Save tokenizer files to {pytorch_dump_path}''' )
							__snake_case     			=       TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"							,    model_max_length=5_12 )
							tokenizer.save_pretrained(SCREAMING_SNAKE_CASE )

							print("Used relative position embeddings:"							,    model.config.reset_position_index_per_cell )


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
		)
		parser.add_argument(
		    """--reset_position_index_per_cell""",
		    default=False,
		    action="""store_true""",
		    help="""Whether to use relative position embeddings or not. Defaults to True.""",
		)
		parser.add_argument(
		    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
		)
		parser.add_argument(
		    """--tapas_config_file""",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        """The config json file corresponding to the pre-trained TAPAS model. \n"""
		        """This specifies the model architecture."""
		    ),
		)
		parser.add_argument(
		    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		_SCREAMING_SNAKE_CASE	       =      parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.task,
		    args.reset_position_index_per_cell,
		    args.tf_checkpoint_path,
		    args.tapas_config_file,
		    args.pytorch_dump_path,
		)
