

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 SCREAMING_SNAKE_CASE	:






			"""simple docstring"""




			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=5_12			,					__UpperCamelCase=0.02			,					):

				"""simple docstring"""


				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				):

				"""simple docstring"""


				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=__UpperCamelCase			,					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				):

				"""simple docstring"""


				snake_case_   		=			self.prepare_config_and_inputs()
				snake_case_		,							snake_case_		,							snake_case_   		=			config_and_inputs
				snake_case_   		=			{'input_ids': input_ids, 'attention_mask': attention_mask}
				return config, inputs_dict


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

				"""simple docstring"""


				snake_case_   		=			20
				snake_case_   		=			model_class_name(__UpperCamelCase				)

				snake_case_   		=			model.init_cache(input_ids.shape[0]			,					__UpperCamelCase				)
				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=__UpperCamelCase			,					past_key_values=__UpperCamelCase			,					position_ids=__UpperCamelCase			,					)

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

				snake_case_   		=			model(__UpperCamelCase				)

				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			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			20
				snake_case_   		=			model_class_name(__UpperCamelCase				)

				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]			,					__UpperCamelCase				)
				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=__UpperCamelCase			,					past_key_values=__UpperCamelCase			,					position_ids=__UpperCamelCase			,					)
				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=__UpperCamelCase			,					position_ids=__UpperCamelCase			,					)

				snake_case_   		=			model(__UpperCamelCase			,					attention_mask=__UpperCamelCase				)

				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	(		__snake_case				,    __snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
			__A    	=	(FlaxGPTJForCausalLM,) if is_flax_available() else ()


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			FlaxGPTJModelTester(self				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				for model_class_name in self.all_model_classes:
					snake_case_		,							snake_case_		,							snake_case_   		=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.check_use_cache_forward(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				for model_class_name in self.all_model_classes:
					snake_case_		,							snake_case_		,							snake_case_   		=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.check_use_cache_forward_with_attn_mask(
					    __UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				)


			@tooslow
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				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=__UpperCamelCase			,					truncation=__UpperCamelCase				)

				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(__UpperCamelCase			,					skip_special_tokens=__UpperCamelCase				)

				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(__UpperCamelCase			,					__UpperCamelCase				)


			@is_pt_flax_cross_test
			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__				):
						# prepare inputs
						snake_case_   		=			self._prepare_for_class(__UpperCamelCase			,					__UpperCamelCase				)
						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(__UpperCamelCase			,					__UpperCamelCase				)

						snake_case_		,							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(__UpperCamelCase				):
							snake_case_   		=			0
							snake_case_   		=			1
							snake_case_   		=			0
							snake_case_   		=			1
						snake_case_   		=			pt_model_class(__UpperCamelCase				).eval()
						snake_case_   		=			model_class(__UpperCamelCase			,					dtype=jnp.floataa				)

						snake_case_   		=			convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,					__UpperCamelCase				)
						snake_case_   		=			fx_state

						with torch.no_grad():
							snake_case_   		=			pt_model(**__UpperCamelCase				).to_tuple()

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

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

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


			@is_pt_flax_cross_test
			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__				):
						# prepare inputs
						snake_case_   		=			self._prepare_for_class(__UpperCamelCase			,					__UpperCamelCase				)
						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(__UpperCamelCase			,					__UpperCamelCase				)

						snake_case_   		=			pt_model_class(__UpperCamelCase				).eval()
						snake_case_   		=			model_class(__UpperCamelCase			,					dtype=jnp.floataa				)

						snake_case_   		=			load_flax_weights_in_pytorch_model(__UpperCamelCase			,					fx_model.params				)
						snake_case_		,							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(__UpperCamelCase				):
							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(**__UpperCamelCase				).to_tuple()

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

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

						with torch.no_grad():
							snake_case_   		=			pt_model_loaded(**__UpperCamelCase				).to_tuple()

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







			@tooslow
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				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(__UpperCamelCase				)



import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class SCREAMING_SNAKE_CASE	(		__snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	None
			__A    	=	BloomTokenizerFast
			__A    	=	BloomTokenizerFast
			__A    	=	True
			__A    	=	False
			__A    	=	"""tokenizer_file"""
			__A    	=	{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				super().setUp()
				snake_case_   		=			BloomTokenizerFast.from_pretrained('bigscience/tokenizer'				)
				tokenizer.save_pretrained(self.tmpdirname				)


			def       __lowerCAmelCase			(     self			,					**__UpperCamelCase				):

				"""simple docstring"""


				kwargs.update(self.special_tokens_map				)
				return BloomTokenizerFast.from_pretrained(self.tmpdirname			,					**__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.get_rust_tokenizer()

				snake_case_   		=			['The quick brown fox</s>', 'jumps over the lazy dog</s>']
				snake_case_   		=			[[21_75, 2_37_14, 7_31_73, 14_42_52, 2], [77, 13_26_19, 34_78, 3_68, 10_95_86, 3_54_33, 2]]

				snake_case_   		=			tokenizer.batch_encode_plus(__UpperCamelCase				)['input_ids']
				self.assertListEqual(__UpperCamelCase			,					__UpperCamelCase				)

				snake_case_   		=			tokenizer.batch_decode(__UpperCamelCase				)
				self.assertListEqual(__UpperCamelCase			,					__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase=6				):

				"""simple docstring"""


				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
						snake_case_   		=			self.rust_tokenizer_class.from_pretrained(__UpperCamelCase			,					**__UpperCamelCase				)
						# tokenizer_r.pad_token = None # Hotfixing padding = None
						# Simple input
						snake_case_   		=			'This is a simple input'
						snake_case_   		=			['This is a simple input 1', 'This is a simple input 2']
						snake_case_   		=			('This is a simple input', 'This is a pair')
						snake_case_   		=			[
						    ('This is a simple input 1', 'This is a simple input 2'),
						    ('This is a simple pair 1', 'This is a simple pair 2'),
						]

						# Simple input tests
						try:
							tokenizer_r.encode(__UpperCamelCase			,					max_length=__UpperCamelCase				)
							tokenizer_r.encode_plus(__UpperCamelCase			,					max_length=__UpperCamelCase				)

							tokenizer_r.batch_encode_plus(__UpperCamelCase			,					max_length=__UpperCamelCase				)
							tokenizer_r.encode(__UpperCamelCase			,					max_length=__UpperCamelCase				)
							tokenizer_r.batch_encode_plus(__UpperCamelCase			,					max_length=__UpperCamelCase				)
						except ValueError:
							self.fail('Bloom Tokenizer should be able to deal with padding'				)

						snake_case_   		=			None  # Hotfixing padding = None
						self.assertRaises(__UpperCamelCase			,					tokenizer_r.encode			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'				)

						# Simple input
						self.assertRaises(__UpperCamelCase			,					tokenizer_r.encode_plus			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'				)

						# Simple input
						self.assertRaises(
						    __UpperCamelCase			,					tokenizer_r.batch_encode_plus			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'			,					)

						# Pair input
						self.assertRaises(__UpperCamelCase			,					tokenizer_r.encode			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'				)

						# Pair input
						self.assertRaises(__UpperCamelCase			,					tokenizer_r.encode_plus			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'				)

						# Pair input
						self.assertRaises(
						    __UpperCamelCase			,					tokenizer_r.batch_encode_plus			,					__UpperCamelCase			,					max_length=__UpperCamelCase			,					padding='max_length'			,					)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.get_rust_tokenizer()
				snake_case_   		=			load_dataset('xnli'			,					'all_languages'			,					split='test'			,					streaming=__UpperCamelCase				)

				snake_case_   		=			next(iter(__UpperCamelCase				)				)['premise']  # pick up one data
				snake_case_   		=			list(sample_data.values()				)

				snake_case_   		=			list(map(tokenizer.encode			,					__UpperCamelCase				)				)
				snake_case_   		=			[tokenizer.decode(__UpperCamelCase			,					clean_up_tokenization_spaces=__UpperCamelCase				) for x in output_tokens]
				self.assertListEqual(__UpperCamelCase			,					__UpperCamelCase				)







			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map				)			,					1				)
				self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()				)[0]				)			,					1				)
