
import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
				from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder



def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A=None       ,     A=None       ,     A=None       ,     A=None       ,     A=None       ,     ):
	if attention_mask is None:
		UpperCAmelCase_       				=			input_ids.ne(config.pad_token_id    )
	if decoder_attention_mask is None:
		UpperCAmelCase_       				=			decoder_input_ids.ne(config.pad_token_id    )
	if head_mask is None:
		UpperCAmelCase_       				=			torch.ones(config.encoder_layers       ,     config.encoder_attention_heads       ,     device=A    )
	if decoder_head_mask is None:
		UpperCAmelCase_       				=			torch.ones(config.decoder_layers       ,     config.decoder_attention_heads       ,     device=A    )
	if cross_attn_head_mask is None:
		UpperCAmelCase_       				=			torch.ones(config.decoder_layers       ,     config.decoder_attention_heads       ,     device=A    )
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": attention_mask,
	    "head_mask": head_mask,
	    "decoder_head_mask": decoder_head_mask,
	    "cross_attn_head_mask": cross_attn_head_mask,
	}

class 						__UpperCamelCase    :




	def __init__(		self	:						Dict		,	lowerCAmelCase	:						List[Any]		,	lowerCAmelCase	:						Tuple=13		,	lowerCAmelCase	:						Tuple=7		,	lowerCAmelCase	:						str=True		,	lowerCAmelCase	:						Optional[int]=False		,	lowerCAmelCase	:						Dict=99		,	lowerCAmelCase	:						Any=16		,	lowerCAmelCase	:						Tuple=2		,	lowerCAmelCase	:						Optional[int]=4		,	lowerCAmelCase	:						List[Any]=4		,	lowerCAmelCase	:						Optional[Any]="relu"		,	lowerCAmelCase	:						Union[str, Any]=0.1		,	lowerCAmelCase	:						Dict=0.1		,	lowerCAmelCase	:						List[str]=0.0		,	lowerCAmelCase	:						Any=0.0		,	lowerCAmelCase	:						Union[str, Any]=20		,	lowerCAmelCase	:						Any=2		,	lowerCAmelCase	:						int=1		,	lowerCAmelCase	:						Union[str, Any]=0		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			parent
		UpperCAmelCase_       				=			batch_size
		UpperCAmelCase_       				=			seq_length
		UpperCAmelCase_       				=			is_training
		UpperCAmelCase_       				=			use_labels
		UpperCAmelCase_       				=			vocab_size
		UpperCAmelCase_       				=			hidden_size
		UpperCAmelCase_       				=			num_hidden_layers
		UpperCAmelCase_       				=			num_attention_heads
		UpperCAmelCase_       				=			intermediate_size
		UpperCAmelCase_       				=			hidden_act
		UpperCAmelCase_       				=			hidden_dropout_prob
		UpperCAmelCase_       				=			attention_probs_dropout_prob
		UpperCAmelCase_       				=			encoder_layerdrop
		UpperCAmelCase_       				=			decoder_layerdrop
		UpperCAmelCase_       				=			max_position_embeddings
		UpperCAmelCase_       				=			eos_token_id
		UpperCAmelCase_       				=			pad_token_id
		UpperCAmelCase_       				=			bos_token_id




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size							)
		UpperCAmelCase_       				=			self.eos_token_id  # Eos Token
		UpperCAmelCase_       				=			ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size							)

		# we need to clamp the input ids here to avoid having pad token in between
		# this is because for M2M100 the position_ids are prepared such that
		# all pad tokens have pos id = 2 and rest are between 2..seq_length
		# and the seq_length here is seq_length - num_pad_tokens
		# but when using past, there is no way of knowing if the past input ids had
		# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
		# position_ids being off by num_pad_tokens in past input
		UpperCAmelCase_       				=			input_ids.clamp(self.pad_token_id + 1							)
		UpperCAmelCase_       				=			decoder_input_ids.clamp(self.pad_token_id + 1							)

		UpperCAmelCase_       				=			self.get_config()
		UpperCAmelCase_       				=			prepare_mam_aaa_inputs_dict(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
		return config, inputs_dict




	def       __A					(		self	:						int							):


		'''simple docstring'''







		return MaMaaaConfig(
		    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		,	encoder_layerdrop=self.encoder_layerdrop		,	decoder_layerdrop=self.decoder_layerdrop		,	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		,	)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







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




	def       __A					(		self	:						Tuple		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			MaMaaaModel(config=lowerCAmelCase							).get_decoder().to(lowerCAmelCase							).eval()
		UpperCAmelCase_       				=			inputs_dict["input_ids"]
		UpperCAmelCase_       				=			inputs_dict["attention_mask"]
		UpperCAmelCase_       				=			inputs_dict["head_mask"]

		# first forward pass
		UpperCAmelCase_       				=			model(lowerCAmelCase		,	attention_mask=lowerCAmelCase		,	head_mask=lowerCAmelCase		,	use_cache=lowerCAmelCase							)

		UpperCAmelCase_				,   UpperCAmelCase_       				=			outputs.to_tuple()

		# create hypothetical multiple next token and extent to next_input_ids
		UpperCAmelCase_       				=			ids_tensor((self.batch_size, 3)		,	config.vocab_size							)
		UpperCAmelCase_       				=			ids_tensor((self.batch_size, 3)		,	2							)

		# append to next input_ids and
		UpperCAmelCase_       				=			torch.cat([input_ids, next_tokens]		,	dim=-1							)
		UpperCAmelCase_       				=			torch.cat([attention_mask, next_attn_mask]		,	dim=-1							)

		UpperCAmelCase_       				=			model(lowerCAmelCase		,	attention_mask=lowerCAmelCase							)["last_hidden_state"]
		UpperCAmelCase_       				=			model(lowerCAmelCase		,	attention_mask=lowerCAmelCase		,	past_key_values=lowerCAmelCase							)[
		    "last_hidden_state"
		]

		# select random slice
		UpperCAmelCase_       				=			ids_tensor((1,)		,	output_from_past.shape[-1]							).item()
		UpperCAmelCase_       				=			output_from_no_past[:, -3:, random_slice_idx].detach()
		UpperCAmelCase_       				=			output_from_past[:, :, random_slice_idx].detach()

		self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

		# test that outputs are equal for slice
		self.parent.assertTrue(torch.allclose(lowerCAmelCase		,	lowerCAmelCase		,	atol=1e-2							)							)




	def       __A					(		self	:						Any		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			MaMaaaModel(config=lowerCAmelCase							).to(lowerCAmelCase							).eval()
		UpperCAmelCase_       				=			model(**lowerCAmelCase							)

		UpperCAmelCase_       				=			outputs.encoder_last_hidden_state
		UpperCAmelCase_       				=			outputs.last_hidden_state

		with tempfile.TemporaryDirectory() as tmpdirname:
			UpperCAmelCase_       				=			model.get_encoder()
			encoder.save_pretrained(lowerCAmelCase							)
			UpperCAmelCase_       				=			MaMaaaEncoder.from_pretrained(lowerCAmelCase							).to(lowerCAmelCase							)

		UpperCAmelCase_       				=			encoder(inputs_dict["input_ids"]		,	attention_mask=inputs_dict["attention_mask"]							)[
		    0
		]

		self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3							)

		with tempfile.TemporaryDirectory() as tmpdirname:
			UpperCAmelCase_       				=			model.get_decoder()
			decoder.save_pretrained(lowerCAmelCase							)
			UpperCAmelCase_       				=			MaMaaaDecoder.from_pretrained(lowerCAmelCase							).to(lowerCAmelCase							)

		UpperCAmelCase_       				=			decoder(
		    input_ids=inputs_dict["decoder_input_ids"]		,	attention_mask=inputs_dict["decoder_attention_mask"]		,	encoder_hidden_states=lowerCAmelCase		,	encoder_attention_mask=inputs_dict["attention_mask"]		,	)[0]

		self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3							)


@require_torch
class 						__UpperCamelCase    (					lowercase     ,     lowercase     ,     lowercase     ,     unittest.TestCase						):
	SCREAMING_SNAKE_CASE__    				=     (
	    (
	        MaMaaaModel,
	        MaMaaaForConditionalGeneration,
	    )
	    if is_torch_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE__    				=     (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
	SCREAMING_SNAKE_CASE__    				=     (
	    {
	        'conversational': MaMaaaForConditionalGeneration,
	        'feature-extraction': MaMaaaModel,
	        'summarization': MaMaaaForConditionalGeneration,
	        'text2text-generation': MaMaaaForConditionalGeneration,
	        'translation': MaMaaaForConditionalGeneration,
	    }
	    if is_torch_available()
	    else {}
	)
	SCREAMING_SNAKE_CASE__    				=     True
	SCREAMING_SNAKE_CASE__    				=     True
	SCREAMING_SNAKE_CASE__    				=     False
	SCREAMING_SNAKE_CASE__    				=     False




	def       __A					(		self	:						int		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						int							):


		'''simple docstring'''







		if pipeline_test_casse_name == "TranslationPipelineTests":
			# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
			# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
			return True

		return False




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			MaMaaaModelTester(self							)
		UpperCAmelCase_       				=			ConfigTester(self		,	config_class=lowerCAmelCase							)




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		self.config_tester.run_common_tests()




	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		for model_class in self.all_model_classes:
			UpperCAmelCase_       				=			model_class(lowerCAmelCase							)

			with tempfile.TemporaryDirectory() as tmpdirname:
				model.save_pretrained(lowerCAmelCase							)
				UpperCAmelCase_				,   UpperCAmelCase_       				=			model_class.from_pretrained(lowerCAmelCase		,	output_loading_info=lowerCAmelCase							)
			self.assertEqual(info["missing_keys"]		,	[]							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCAmelCase							)




	def       __A					(		self	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs_for_common()
		self.model_tester.check_encoder_decoder_model_standalone(*lowerCAmelCase							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
			UpperCAmelCase_       				=			model_class(lowerCAmelCase							)
			model.to(lowerCAmelCase							)
			model.eval()

			UpperCAmelCase_       				=			copy.deepcopy(self._prepare_for_class(lowerCAmelCase		,	lowerCAmelCase							)							)

			if not self.is_encoder_decoder:
				UpperCAmelCase_       				=			inputs["input_ids"]
				del inputs["input_ids"]
			else:
				UpperCAmelCase_       				=			inputs["input_ids"]
				UpperCAmelCase_       				=			inputs.get("decoder_input_ids"		,	lowerCAmelCase							)
				del inputs["input_ids"]
				inputs.pop("decoder_input_ids"		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			model.get_input_embeddings()
			if not self.is_encoder_decoder:
				UpperCAmelCase_       				=			wte(lowerCAmelCase							)
			else:
				UpperCAmelCase_       				=			wte(lowerCAmelCase							)
				UpperCAmelCase_       				=			wte(lowerCAmelCase							)

			with torch.no_grad():
				model(**lowerCAmelCase							)[0]




	def       __A					(		self	:						int							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		UpperCAmelCase_       				=			input_dict["input_ids"]
		UpperCAmelCase_       				=			input_ids.ne(1							).to(lowerCAmelCase							)
		UpperCAmelCase_       				=			MaMaaaForConditionalGeneration(lowerCAmelCase							).eval().to(lowerCAmelCase							)
		if torch_device == "cuda":
			model.half()
		model.generate(lowerCAmelCase		,	attention_mask=lowerCAmelCase							)
		model.generate(num_beams=4		,	do_sample=lowerCAmelCase		,	early_stopping=lowerCAmelCase		,	num_return_sequences=3							)



def       __lowerCAmelCase      (						A    ):
	return torch.tensor(A       ,     dtype=torch.long       ,     device=A    )


_a:    Optional[int] 							=			1e-4




@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 						__UpperCamelCase    (					unittest.TestCase						):




	@cached_property
	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"							)




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			MaMaaaModel.from_pretrained("facebook/m2m100_418M"							).to(lowerCAmelCase							)
		UpperCAmelCase_       				=			_long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]]							)
		UpperCAmelCase_       				=			_long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]]							)
		UpperCAmelCase_       				=			prepare_mam_aaa_inputs_dict(model.config		,	lowerCAmelCase		,	lowerCAmelCase							)
		with torch.no_grad():
			UpperCAmelCase_       				=			model(**lowerCAmelCase							)[0]
		UpperCAmelCase_       				=			torch.Size((1, 11, 1_024)							)
		self.assertEqual(output.shape		,	lowerCAmelCase							)
		# change to expected output here
		UpperCAmelCase_       				=			torch.tensor(
		    [[-0.7_780, -0.1_676, 0.1_038], [-6.7_556, -1.3_992, 0.0_567], [-7.5_383, -0.5_920, -0.2_779]]		,	device=lowerCAmelCase							)
		self.assertTrue(torch.allclose(output[:, :3, :3]		,	lowerCAmelCase		,	atol=lowerCAmelCase							)							)




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"							).to(lowerCAmelCase							)

		# change to intended input
		UpperCAmelCase_       				=			_long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]]							)
		UpperCAmelCase_       				=			_long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]]							)
		UpperCAmelCase_       				=			prepare_mam_aaa_inputs_dict(model.config		,	lowerCAmelCase		,	lowerCAmelCase							)
		with torch.no_grad():
			UpperCAmelCase_       				=			model(**lowerCAmelCase							)[0]
		UpperCAmelCase_       				=			torch.Size((1, 11, model.config.vocab_size)							)
		self.assertEqual(output.shape		,	lowerCAmelCase							)
		# change to expected output here
		UpperCAmelCase_       				=			torch.tensor(
		    [[-1.0_448, -1.0_411, 3.7_992], [-3.2_191, -3.2_386, -1.3_451], [-3.6_210, -3.5_993, 0.4_925]]		,	device=lowerCAmelCase							)
		self.assertTrue(torch.allclose(output[:, :3, :3]		,	lowerCAmelCase		,	atol=lowerCAmelCase							)							)




	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"							).to(lowerCAmelCase							)
		UpperCAmelCase_       				=			MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"		,	src_lang="fr"		,	tgt_lang="en"							)

		UpperCAmelCase_       				=			[
		    "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
		    "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
		    "Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
		    " Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
		    " l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
		]

		# The below article tests that we don't add any hypotheses outside of the top n_beams
		UpperCAmelCase_       				=			tokenizer(lowerCAmelCase		,	padding=lowerCAmelCase		,	return_tensors="pt"							)

		UpperCAmelCase_       				=			model.generate(
		    input_ids=dct["input_ids"].to(lowerCAmelCase							)		,	attention_mask=dct["attention_mask"].to(lowerCAmelCase							)		,	num_beams=5		,	forced_bos_token_id=tokenizer.get_lang_id("en"							)		,	)

		UpperCAmelCase_       				=			[
		    "The NSA case highlights the total absence of intelligence debate",
		    "I think there are two levels of response from the French government.",
		    "When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
		    " Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
		    " communications in France.",
		]

		UpperCAmelCase_       				=			tokenizer.batch_decode(
		    hypotheses_batch.tolist()		,	clean_up_tokenization_spaces=lowerCAmelCase		,	skip_special_tokens=lowerCAmelCase							)
		assert generated == expected_en


from __future__ import annotations

class 						__UpperCamelCase    :




	def __init__(		self	:						Optional[Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_				,   UpperCAmelCase_       				=			text, pattern
		UpperCAmelCase_				,   UpperCAmelCase_       				=			len(lowerCAmelCase							), len(lowerCAmelCase							)




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		for i in range(self.patLen - 1		,	-1		,	-1							):
			if char == self.pattern[i]:
				return i
		return -1




	def       __A					(		self	:						int		,	lowerCAmelCase	:						int							):


		'''simple docstring'''







		for i in range(self.patLen - 1		,	-1		,	-1							):
			if self.pattern[i] != self.text[current_pos + i]:
				return current_pos + i
		return -1




	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			[]
		for i in range(self.textLen - self.patLen + 1							):
			UpperCAmelCase_       				=			self.mismatch_in_text(lowerCAmelCase							)
			if mismatch_index == -1:
				positions.append(lowerCAmelCase							)
			else:
				UpperCAmelCase_       				=			self.match_in_pattern(self.text[mismatch_index]							)
				UpperCAmelCase_       				=			(
				    mismatch_index - match_index
				)  # shifting index lgtm [py/multiple-definition]
		return positions


_a:    Dict 							=			"""ABAABA"""
_a:    Union[str, Any] 							=			"""AB"""
_a:    str 							=			BoyerMooreSearch(text, pattern)
_a:    Dict 							=			bms.bad_character_heuristic()

if len(positions) == 0:
				print("""No match found""")
else:
				print("""Pattern found in following positions: """)
				print(positions)