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  :     Tuple       =  """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 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase=None							,			lowercase=None							,			lowercase=None							,			lowercase=None							,			lowercase=None							,			lowercase=None							,			):







				"""simple docstring"""



				if attention_mask is None:
								__lowercase 			=							np.where(input_ids != config.pad_token_id							,			1							,			0							)
				if decoder_attention_mask is None:
								__lowercase 			=							np.where(decoder_input_ids != config.pad_token_id							,			1							,			0							)
				if head_mask is None:
								__lowercase 			=							np.ones((config.encoder_layers, config.encoder_attention_heads)							)
				if decoder_head_mask is None:
								__lowercase 			=							np.ones((config.decoder_layers, config.decoder_attention_heads)							)
				if cross_attn_head_mask is None:
								__lowercase 			=							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    _UpperCamelCase      :


			"""simple docstring"""






			def __init__(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__=13      ,   lowerCAmelCase__=7      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=99      ,   lowerCAmelCase__=16      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=4      ,   lowerCAmelCase__=4      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=32      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=0      ,   lowerCAmelCase__=0.02      ,   )     ->					Union[str, Any]:

							'''simple docstring'''


							__lowercase 			=							parent
							__lowercase 			=							batch_size
							__lowercase 			=							seq_length
							__lowercase 			=							is_training
							__lowercase 			=							use_labels
							__lowercase 			=							vocab_size
							__lowercase 			=							hidden_size
							__lowercase 			=							num_hidden_layers
							__lowercase 			=							num_attention_heads
							__lowercase 			=							intermediate_size
							__lowercase 			=							hidden_act
							__lowercase 			=							hidden_dropout_prob
							__lowercase 			=							attention_probs_dropout_prob
							__lowercase 			=							max_position_embeddings
							__lowercase 			=							eos_token_id
							__lowercase 			=							pad_token_id
							__lowercase 			=							bos_token_id
							__lowercase 			=							initializer_range





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Union[str, Any]:

							'''simple docstring'''


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

							__lowercase 			=							shift_tokens_right(lowerCAmelCase__      ,   1      ,   2						)

							__lowercase 			=							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__      ,   )
							__lowercase 			=							prepare_blenderbot_inputs_dict(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
							return config, inputs_dict





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					List[Any]:

							'''simple docstring'''


							__lowercase 			=							20
							__lowercase 			=							model_class_name(lowerCAmelCase__						)

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

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

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

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

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

							__lowercase 			=							model.decode(lowerCAmelCase__      ,   lowerCAmelCase__						)

							__lowercase 			=							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      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)     ->					Optional[Any]:

							'''simple docstring'''


							__lowercase 			=							20
							__lowercase 			=							model_class_name(lowerCAmelCase__						)

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

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

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

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

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

							__lowercase 			=							model.decode(lowerCAmelCase__      ,   lowerCAmelCase__      ,   decoder_attention_mask=lowerCAmelCase__						)

							__lowercase 			=							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    _UpperCamelCase      (						unittest.TestCase ):


			"""simple docstring"""






			__a  : List[Any]			 =						99





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							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      ,   )

							__lowercase 			=							input_ids.shape[0]
							__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							__lowercase 			=							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      ,   )
							__lowercase 			=							FlaxBlenderbotForConditionalGeneration(lowerCAmelCase__						)
							__lowercase 			=							np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]      ,   dtype=np.intaa						)
							__lowercase 			=							np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]      ,   dtype=np.intaa						)
							__lowercase 			=							lm_model(input_ids=lowerCAmelCase__      ,   decoder_input_ids=lowerCAmelCase__						)
							__lowercase 			=							(*summary.shape, config.vocab_size)
							self.assertEqual(outputs['''logits'''].shape      ,   lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[Any]:

							'''simple docstring'''


							__lowercase 			=							np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]      ,   dtype=np.intaa						)
							__lowercase 			=							shift_tokens_right(lowerCAmelCase__      ,   1      ,   2						)
							__lowercase 			=							np.equal(lowerCAmelCase__      ,   1						).astype(np.floataa						).sum()
							__lowercase 			=							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    _UpperCamelCase      (						_UpperCAmelCase						,unittest.TestCase						,_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Any			 =						True
			__a  : Any			 =						(
			    (
			        FlaxBlenderbotModel,
			        FlaxBlenderbotForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			__a  : Optional[Any]			 =						(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							FlaxBlenderbotModelTester(self						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[Any]:

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__						):
															__lowercase 			=							self._prepare_for_class(lowerCAmelCase__      ,   lowerCAmelCase__						)
															__lowercase 			=							model_class(lowerCAmelCase__						)

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

															with self.subTest('''JIT Enabled'''						):
																			__lowercase 			=							encode_jitted(**lowerCAmelCase__						).to_tuple()

															with self.subTest('''JIT Disabled'''						):
																			with jax.disable_jit():
																							__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							__lowercase			,     __lowercase 			=							self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__						):
															__lowercase 			=							model_class(lowerCAmelCase__						)
															__lowercase 			=							model.encode(inputs_dict['''input_ids''']      ,   inputs_dict['''attention_mask''']						)

															__lowercase 			=							{
															    '''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(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						):
																			return model.decode(
																			    decoder_input_ids=lowerCAmelCase__      ,   decoder_attention_mask=lowerCAmelCase__      ,   encoder_outputs=lowerCAmelCase__      ,   )

															with self.subTest('''JIT Enabled'''						):
																			__lowercase 			=							decode_jitted(**lowerCAmelCase__						).to_tuple()

															with self.subTest('''JIT Disabled'''						):
																			with jax.disable_jit():
																							__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


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





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

							'''simple docstring'''


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

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

							__lowercase 			=							['''Sam''']
							__lowercase 			=							tokenizer(lowerCAmelCase__      ,   return_tensors='''jax'''						)

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

							__lowercase 			=							tokenizer.batch_decode(lowerCAmelCase__      ,   **lowerCAmelCase__						)
							assert generated_txt[0].strip() == tgt_text
import requests





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase 			=							{'''Content-Type''': '''application/json'''}
				__lowercase 			=							requests.post(lowercase							,			json={'''text''': message_body}							,			headers=lowercase							)
				if response.status_code != 200:
								__lowercase 			=							(
								    '''Request to slack returned an error '''
								    F"{response.status_code}, the response is:\n{response.text}"
								)
								raise ValueError(lowercase							)


if __name__ == "__main__":
		# Set the slack url to the one provided by Slack when you create the webhook at
		# https://my.slack.com/services/new/incoming-webhook/
		send_slack_message("""<YOUR MESSAGE BODY>""", """<SLACK CHANNEL URL>""")