

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

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


if is_flax_available():
		import os

		# The slow tests are often failing with OOM error on GPU
		# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
		# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
		A											=     'platform'

		import jax
		import jax.numpy as jnp

		from transformers import BlenderbotTokenizer
		from transformers.models.blenderbot.modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    shift_tokens_right,
		)





def a(lowercase__			, lowercase__			, lowercase__=None			, lowercase__=None			, lowercase__=None			, lowercase__=None			, lowercase__=None			, lowercase__=None			, ):





	'''simple docstring'''
	if attention_mask is None:
		snake_case_      		= np.where(input_ids != config.pad_token_id			, 1			, 0	)
	if decoder_attention_mask is None:
		snake_case_      		= np.where(decoder_input_ids != config.pad_token_id			, 1			, 0	)
	if head_mask is None:
		snake_case_      		= np.ones((config.encoder_layers, config.encoder_attention_heads)	)
	if decoder_head_mask is None:
		snake_case_      		= np.ones((config.decoder_layers, config.decoder_attention_heads)	)
	if cross_attn_head_mask is None:
		snake_case_      		= np.ones((config.decoder_layers, config.decoder_attention_heads)	)
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": attention_mask,
	}


class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase=13 ,				__UpperCamelCase=7 ,				__UpperCamelCase=True ,				__UpperCamelCase=False ,				__UpperCamelCase=99 ,				__UpperCamelCase=16 ,				__UpperCamelCase=2 ,				__UpperCamelCase=4 ,				__UpperCamelCase=4 ,				__UpperCamelCase="gelu" ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=32 ,				__UpperCamelCase=2 ,				__UpperCamelCase=1 ,				__UpperCamelCase=0 ,				__UpperCamelCase=0.02 ,				):

				"""simple docstring"""


				snake_case_      		= parent
				snake_case_      		= batch_size
				snake_case_      		= seq_length
				snake_case_      		= is_training
				snake_case_      		= use_labels
				snake_case_      		= vocab_size
				snake_case_      		= hidden_size
				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_      		= eos_token_id
				snake_case_      		= pad_token_id
				snake_case_      		= bos_token_id
				snake_case_      		= initializer_range


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,				self.vocab_size							) ,				3 ,				self.vocab_size							)
				snake_case_      		= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,				dtype=np.intaa							)) ,				-1							)

				snake_case_      		= shift_tokens_right(_lowerCamelCase ,				1 ,				2							)

				snake_case_      		= BlenderbotConfig(
				    vocab_size=self.vocab_size ,				d_model=self.hidden_size ,				encoder_layers=self.num_hidden_layers ,				decoder_layers=self.num_hidden_layers ,				encoder_attention_heads=self.num_attention_heads ,				decoder_attention_heads=self.num_attention_heads ,				encoder_ffn_dim=self.intermediate_size ,				decoder_ffn_dim=self.intermediate_size ,				dropout=self.hidden_dropout_prob ,				attention_dropout=self.attention_probs_dropout_prob ,				max_position_embeddings=self.max_position_embeddings ,				eos_token_id=self.eos_token_id ,				bos_token_id=self.bos_token_id ,				pad_token_id=self.pad_token_id ,				initializer_range=self.initializer_range ,				use_cache=_lowerCamelCase ,				)
				snake_case_      		= prepare_blenderbot_inputs_dict(_lowerCamelCase ,				_lowerCamelCase ,				_lowerCamelCase							)
				return config, inputs_dict


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.prepare_config_and_inputs()
				return config, inputs_dict


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= 20
				snake_case_      		= model_class_name(_lowerCamelCase							)

				snake_case_      		= model.encode(inputs_dict['input_ids']							)

				snake_case_		,       snake_case_      		= (
				    inputs_dict['decoder_input_ids'],
				    inputs_dict['decoder_attention_mask'],
				)

				snake_case_      		= model.init_cache(decoder_input_ids.shape[0] ,				_lowerCamelCase ,				_lowerCamelCase							)
				snake_case_      		= jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,				dtype='i4'							)

				snake_case_      		= jnp.broadcast_to(
				    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :] ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,				)
				snake_case_      		= model.decode(
				    decoder_input_ids[:, :-1] ,				_lowerCamelCase ,				decoder_attention_mask=_lowerCamelCase ,				past_key_values=_lowerCamelCase ,				decoder_position_ids=_lowerCamelCase ,				)

				snake_case_      		= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,				dtype='i4'							)
				snake_case_      		= model.decode(
				    decoder_input_ids[:, -1:] ,				_lowerCamelCase ,				decoder_attention_mask=_lowerCamelCase ,				past_key_values=outputs_cache.past_key_values ,				decoder_position_ids=_lowerCamelCase ,				)

				snake_case_      		= model.decode(_lowerCamelCase ,				_lowerCamelCase							)

				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 ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= 20
				snake_case_      		= model_class_name(_lowerCamelCase							)

				snake_case_      		= model.encode(inputs_dict['input_ids']							)

				snake_case_		,       snake_case_      		= (
				    inputs_dict['decoder_input_ids'],
				    inputs_dict['decoder_attention_mask'],
				)

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

				snake_case_      		= model.init_cache(decoder_input_ids.shape[0] ,				_lowerCamelCase ,				_lowerCamelCase							)
				snake_case_      		= jnp.broadcast_to(
				    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :] ,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,				)

				snake_case_      		= model.decode(
				    decoder_input_ids[:, :-1] ,				_lowerCamelCase ,				decoder_attention_mask=_lowerCamelCase ,				past_key_values=_lowerCamelCase ,				decoder_position_ids=_lowerCamelCase ,				)
				snake_case_      		= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,				dtype='i4'							)
				snake_case_      		= model.decode(
				    decoder_input_ids[:, -1:] ,				_lowerCamelCase ,				past_key_values=outputs_cache.past_key_values ,				decoder_attention_mask=_lowerCamelCase ,				decoder_position_ids=_lowerCamelCase ,				)

				snake_case_      		= model.decode(_lowerCamelCase ,				_lowerCamelCase ,				decoder_attention_mask=_lowerCamelCase							)

				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 SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			__A						  =  9_9


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= np.array(
				    [
				        [71, 82, 18, 33, 46, 91, 2],
				        [68, 34, 26, 58, 30, 82, 2],
				        [5, 97, 17, 39, 94, 40, 2],
				        [76, 83, 94, 25, 70, 78, 2],
				        [87, 59, 41, 35, 48, 66, 2],
				        [55, 13, 16, 58, 5, 2, 1],  # note padding
				        [64, 27, 31, 51, 12, 75, 2],
				        [52, 64, 86, 17, 83, 39, 2],
				        [48, 61, 9, 24, 71, 82, 2],
				        [26, 1, 60, 48, 22, 13, 2],
				        [21, 5, 62, 28, 14, 76, 2],
				        [45, 98, 37, 86, 59, 48, 2],
				        [70, 70, 50, 9, 28, 0, 2],
				    ] ,				dtype=np.intaa ,				)

				snake_case_      		= input_ids.shape[0]
				snake_case_      		= BlenderbotConfig(
				    vocab_size=self.vocab_size ,				d_model=24 ,				encoder_layers=2 ,				decoder_layers=2 ,				encoder_attention_heads=2 ,				decoder_attention_heads=2 ,				encoder_ffn_dim=32 ,				decoder_ffn_dim=32 ,				max_position_embeddings=48 ,				eos_token_id=2 ,				pad_token_id=1 ,				bos_token_id=0 ,				)
				return config, input_ids, batch_size


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_		,       snake_case_      		= self._get_config_and_data()
				snake_case_      		= FlaxBlenderbotForConditionalGeneration(_lowerCamelCase							)
				snake_case_      		= lm_model(input_ids=_lowerCamelCase							)
				snake_case_      		= (batch_size, input_ids.shape[1], config.vocab_size)
				self.assertEqual(outputs['logits'].shape ,				_lowerCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= BlenderbotConfig(
				    vocab_size=self.vocab_size ,				d_model=14 ,				encoder_layers=2 ,				decoder_layers=2 ,				encoder_attention_heads=2 ,				decoder_attention_heads=2 ,				encoder_ffn_dim=8 ,				decoder_ffn_dim=8 ,				max_position_embeddings=48 ,				)
				snake_case_      		= FlaxBlenderbotForConditionalGeneration(_lowerCamelCase							)
				snake_case_      		= np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] ,				dtype=np.intaa							)
				snake_case_      		= np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] ,				dtype=np.intaa							)
				snake_case_      		= lm_model(input_ids=_lowerCamelCase ,				decoder_input_ids=_lowerCamelCase							)
				snake_case_      		= (*summary.shape, config.vocab_size)
				self.assertEqual(outputs['logits'].shape ,				_lowerCamelCase							)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] ,				dtype=np.intaa							)
				snake_case_      		= shift_tokens_right(_lowerCamelCase ,				1 ,				2							)
				snake_case_      		= np.equal(_lowerCamelCase ,				1							).astype(np.floataa							).sum()
				snake_case_      		= np.equal(_lowerCamelCase ,				1							).astype(np.floataa							).sum()
				self.assertEqual(shifted.shape ,				input_ids.shape							)
				self.assertEqual(_lowerCamelCase ,				n_pad_before - 1							)
				self.assertTrue(np.equal(shifted[:, 0] ,				2							).all()							)







@require_flax
class SCREAMING_SNAKE_CASE  (		__UpperCAmelCase							,						unittest.TestCase							,						__UpperCAmelCase							):






			"""simple docstring"""




			__A						  =  True
			__A						  =  (
			    (
			        FlaxBlenderbotModel,
			        FlaxBlenderbotForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			__A						  =  (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= FlaxBlenderbotModelTester(self							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs()
				for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward(_lowerCamelCase ,				_lowerCamelCase ,				_lowerCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs()
				for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward_with_attn_mask(_lowerCamelCase ,				_lowerCamelCase ,				_lowerCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       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__							):
						snake_case_      		= self._prepare_for_class(_lowerCamelCase ,				_lowerCamelCase							)
						snake_case_      		= model_class(_lowerCamelCase							)

						@jax.jit
						def encode_jitted(__UpperCamelCase ,				__UpperCamelCase=None ,				**__UpperCamelCase							):
							return model.encode(input_ids=_lowerCamelCase ,				attention_mask=_lowerCamelCase							)

						with self.subTest('JIT Enabled'							):
							snake_case_      		= encode_jitted(**_lowerCamelCase							).to_tuple()

						with self.subTest('JIT Disabled'							):
							with jax.disable_jit():
								snake_case_      		= encode_jitted(**_lowerCamelCase							).to_tuple()

						self.assertEqual(len(_lowerCamelCase							) ,				len(_lowerCamelCase							)							)
						for jitted_output, output in zip(_lowerCamelCase ,				_lowerCamelCase							):
							self.assertEqual(jitted_output.shape ,				output.shape							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       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__							):
						snake_case_      		= model_class(_lowerCamelCase							)
						snake_case_      		= model.encode(inputs_dict['input_ids'] ,				inputs_dict['attention_mask']							)

						snake_case_      		= {
						    'decoder_input_ids': inputs_dict['decoder_input_ids'],
						    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
						    'encoder_outputs': encoder_outputs,
						}

						@jax.jit
						def decode_jitted(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):
							return model.decode(
							    decoder_input_ids=_lowerCamelCase ,				decoder_attention_mask=_lowerCamelCase ,				encoder_outputs=_lowerCamelCase ,				)

						with self.subTest('JIT Enabled'							):
							snake_case_      		= decode_jitted(**_lowerCamelCase							).to_tuple()

						with self.subTest('JIT Disabled'							):
							with jax.disable_jit():
								snake_case_      		= decode_jitted(**_lowerCamelCase							).to_tuple()

						self.assertEqual(len(_lowerCamelCase							) ,				len(_lowerCamelCase							)							)
						for jitted_output, output in zip(_lowerCamelCase ,				_lowerCamelCase							):
							self.assertEqual(jitted_output.shape ,				output.shape							)


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for model_class_name in self.all_model_classes:
					snake_case_      		= model_class_name.from_pretrained('facebook/blenderbot-400M-distill'							)
					# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
					snake_case_      		= np.ones((1, 1)							) * model.config.eos_token_id
					snake_case_      		= model(_lowerCamelCase							)
					self.assertIsNotNone(_lowerCamelCase							)







			@unittest.skipUnless(jax_device != 'cpu' ,				'3B test too slow on CPU.'							)
			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= {'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
				snake_case_      		= {'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

				snake_case_      		= FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B' ,				from_pt=_lowerCamelCase							)
				snake_case_      		= BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'							)

				snake_case_      		= ['Sam']
				snake_case_      		= tokenizer(_lowerCamelCase ,				return_tensors='jax'							)

				snake_case_      		= model.generate(**_lowerCamelCase ,				**_lowerCamelCase							)
				snake_case_      		= 'Sam is a great name. It means "sun" in Gaelic.'

				snake_case_      		= tokenizer.batch_decode(_lowerCamelCase ,				**_lowerCamelCase							)
				assert generated_txt[0].strip() == tgt_text



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 a(lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	# Initialise PyTorch model.
	# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
	# TapasConfig to False.

	# initialize configuration from json file
	snake_case_      		= TapasConfig.from_json_file(lowercase__	)
	# 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=lowercase__	)
	elif task == "WTQ":
		# run_task_main.py hparams
		snake_case_      		= 4
		snake_case_      		= True
		# hparam_utils.py hparams
		snake_case_      		= 0.66_4694
		snake_case_      		= 0.20_7951
		snake_case_      		= 0.12_1194
		snake_case_      		= True
		snake_case_      		= True
		snake_case_      		= False
		snake_case_      		= 0.035_2513

		snake_case_      		= TapasForQuestionAnswering(config=lowercase__	)
	elif task == "WIKISQL_SUPERVISED":
		# run_task_main.py hparams
		snake_case_      		= 4
		snake_case_      		= False
		# hparam_utils.py hparams
		snake_case_      		= 36.4519
		snake_case_      		= 0.90_3421
		snake_case_      		= 222.088
		snake_case_      		= True
		snake_case_      		= True
		snake_case_      		= True
		snake_case_      		= 0.76_3141

		snake_case_      		= TapasForQuestionAnswering(config=lowercase__	)
	elif task == "TABFACT":
		snake_case_      		= TapasForSequenceClassification(config=lowercase__	)
	elif task == "MLM":
		snake_case_      		= TapasForMaskedLM(config=lowercase__	)
	elif task == "INTERMEDIATE_PRETRAINING":
		snake_case_      		= TapasModel(config=lowercase__	)
	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(lowercase__			, lowercase__			, lowercase__	)

	# Save pytorch-model (weights and configuration)
	print(f"""Save PyTorch model to {pytorch_dump_path}"""	)
	model.save_pretrained(lowercase__	)

	# 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=512	)
	tokenizer.save_pretrained(lowercase__	)

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


if __name__ == "__main__":
		A											=     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.'
		)
		A											=     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,
		)
