
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict

import requests
from slack_sdk import WebClient


lowercase   =	WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str							)	->     List[str]:
							lowerCamelCase_					=       test_results.split(""" """							)

							lowerCamelCase_					=       0
							lowerCamelCase_					=       0

							# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
							# When it is too long, those signs are not present.
							lowerCamelCase_					=       expressions[-2] if """=""" in expressions[-1] else expressions[-1]

							for i, expression in enumerate(UpperCAmelCase__							):
														if "failed" in expression:
																					failed += int(expressions[i - 1]							)
														if "passed" in expression:
																					success += int(expressions[i - 1]							)

							return failed, success, time_spent



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Tuple							)	->     Tuple:
							lowerCamelCase_					=       {}
							lowerCamelCase_					=       None
							lowerCamelCase_					=       False
							for line in failures_short_lines.split("""\n"""							):
														if re.search(R"""_ \[doctest\]"""      ,					UpperCAmelCase__							):
																					lowerCamelCase_					=       True
																					lowerCamelCase_					=       line.split(""" """							)[2]
														elif in_error and not line.split(""" """							)[0].isdigit():
																					lowerCamelCase_					=       line
																					lowerCamelCase_					=       False

							return failures





class    __A:

					def __init__(						self       :  List[str] ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Dict ):
												lowerCamelCase_					=       title

												lowerCamelCase_					=       doc_test_results["""time_spent"""].split(""",""" )[0]
												lowerCamelCase_					=       doc_test_results["""success"""]
												lowerCamelCase_					=       doc_test_results["""failures"""]
												lowerCamelCase_					=       self.n_success + self.n_failures

												# Failures and success of the modeling tests
												lowerCamelCase_					=       doc_test_results

					@property
					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       [self._time_spent]
												lowerCamelCase_					=       0

												for time in time_spent:
																			lowerCamelCase_					=       time.split(""":""" )

																			# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
																			if len(__UpperCamelCase ) == 1:
																										lowerCamelCase_					=       [0, 0, time_parts[0]]

																			lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_					=       int(time_parts[0] ), int(time_parts[1] ), float(time_parts[2] )
																			total_secs += hours * 3_6_0_0 + minutes * 6_0 + seconds

												lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_					=       total_secs // 3_6_0_0, (total_secs % 3_6_0_0) // 6_0, total_secs % 6_0
												return F'''{int(__UpperCamelCase )}h{int(__UpperCamelCase )}m{int(__UpperCamelCase )}s'''

					@property
					def  lowercase__   (						self       :  Optional[Any] ):
												return {"type": "header", "text": {"type": "plain_text", "text": self.title}}

					@property
					def  lowercase__   (						self       :  List[str] ):
												return {
												    "type": "section",
												    "text": {
												        "type": "plain_text",
												        "text": F'''🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.''',
												        "emoji": True,
												    },
												    "accessory": {
												        "type": "button",
												        "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
												        "url": F'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
												    },
												}

					@property
					def  lowercase__   (						self       :  Dict ):
												return {
												    "type": "section",
												    "text": {
												        "type": "plain_text",
												        "text": (
												            F'''There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in'''
												            F''' {self.time}.'''
												        ),
												        "emoji": True,
												    },
												    "accessory": {
												        "type": "button",
												        "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
												        "url": F'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
												    },
												}

					@property
					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       4_0
												lowerCamelCase_					=       {k: v["""failed"""] for k, v in doc_test_results.items() if isinstance(__UpperCamelCase ,       __UpperCamelCase )}

												lowerCamelCase_					=       """"""
												for category, failures in category_failures.items():
																			if len(__UpperCamelCase ) == 0:
																										continue

																			if report != "":
																										report += "\n\n"

																			report += F'''*{category} failures*:'''.ljust(line_length // 2 ).rjust(line_length // 2 ) + "\n"
																			report += "`"
																			report += "`\n`".join(__UpperCamelCase )
																			report += "`"

												return {
												    "type": "section",
												    "text": {
												        "type": "mrkdwn",
												        "text": F'''The following examples had failures:\n\n\n{report}\n''',
												    },
												}

					@property
					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       [self.header]

												if self.n_failures > 0:
																			blocks.append(self.failures )

												if self.n_failures > 0:
																			blocks.extend([self.category_failures] )

												if self.n_failures == 0:
																			blocks.append(self.no_failures )

												return json.dumps(__UpperCamelCase )

					@staticmethod
					def  lowercase__   (						):
												lowerCamelCase_					=       [
												    {
												        """type""": """section""",
												        """text""": {
												            """type""": """plain_text""",
												            """text""": """There was an issue running the tests.""",
												        },
												        """accessory""": {
												            """type""": """button""",
												            """text""": {"""type""": """plain_text""", """text""": """Check Action results""", """emoji""": True},
												            """url""": F'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
												        },
												    }
												]

												print("""Sending the following payload""" )
												print(json.dumps({"""blocks""": json.loads(__UpperCamelCase )} ) )

												client.chat_postMessage(
												    channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""] ,       text="""There was an issue running the tests.""" ,       blocks=__UpperCamelCase ,       )

					def  lowercase__   (						self       :  List[Any] ):
												print("""Sending the following payload""" )
												print(json.dumps({"""blocks""": json.loads(self.payload )} ) )

												lowerCamelCase_					=       F'''{self.n_failures} failures out of {self.n_tests} tests,''' if self.n_failures else """All tests passed."""

												lowerCamelCase_					=       client.chat_postMessage(
												    channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""] ,       blocks=self.payload ,       text=__UpperCamelCase ,       )

					def  lowercase__   (						self       :  int ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  Optional[int] ):
												lowerCamelCase_					=       """"""
												for key, value in failures.items():
																			lowerCamelCase_					=       value[:2_0_0] + """ [Truncated]""" if len(__UpperCamelCase ) > 2_5_0 else value
																			failures_text += F'''*{key}*\n_{value}_\n\n'''

												lowerCamelCase_					=       job_name
												lowerCamelCase_					=       {"""type""": """section""", """text""": {"""type""": """mrkdwn""", """text""": text}}

												if job_link is not None:
																			lowerCamelCase_					=       {
																			    """type""": """button""",
																			    """text""": {"""type""": """plain_text""", """text""": """GitHub Action job""", """emoji""": True},
																			    """url""": job_link,
																			}

												return [
												    {"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
												    content,
												    {"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
												]



					def  lowercase__   (						self       :  List[Any] ):
												if self.thread_ts is None:
																			raise ValueError("""Can only post reply if a post has been made.""" )

												lowerCamelCase_					=       self.doc_test_results.pop("""job_link""" )
												self.doc_test_results.pop("""failures""" )
												self.doc_test_results.pop("""success""" )
												self.doc_test_results.pop("""time_spent""" )

												lowerCamelCase_					=       sorted(self.doc_test_results.items() ,       key=lambda __UpperCamelCase : t[0] )
												for job, job_result in sorted_dict:
																			if len(job_result["""failures"""] ):
																										lowerCamelCase_					=       F'''*Num failures* :{len(job_result['failed'] )} \n'''
																										lowerCamelCase_					=       job_result["""failures"""]
																										lowerCamelCase_					=       self.get_reply_blocks(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       text=__UpperCamelCase )

																										print("""Sending the following reply""" )
																										print(json.dumps({"""blocks""": blocks} ) )

																										client.chat_postMessage(
																										    channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""] ,       text=F'''Results for {job}''' ,       blocks=__UpperCamelCase ,       thread_ts=self.thread_ts["""ts"""] ,       )

																										time.sleep(1 )



def   __lowerCAmelCase    (				)	->     List[str]:
							lowerCamelCase_					=       os.environ["""GITHUB_RUN_ID"""]
							lowerCamelCase_					=       F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'''
							lowerCamelCase_					=       requests.get(UpperCAmelCase__							).json()
							lowerCamelCase_					=       {}

							try:
														jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}							)
														lowerCamelCase_					=       math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0							)

														for i in range(UpperCAmelCase__							):
																					lowerCamelCase_					=       requests.get(url + F'''&page={i + 2}'''							).json()
																					jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}							)

														return jobs
							except Exception as e:
														print("""Unknown error, could not fetch links."""      ,					UpperCAmelCase__							)

							return {}



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str							)	->     List[str]:
							lowerCamelCase_					=       {}

							if os.path.exists(UpperCAmelCase__							):
														lowerCamelCase_					=       os.listdir(UpperCAmelCase__							)
														for file in files:
																					try:
																												with open(os.path.join(UpperCAmelCase__      ,					UpperCAmelCase__							)      ,					encoding="""utf-8"""							) as f:
																																			lowerCamelCase_					=       f.read()
																					except UnicodeDecodeError as e:
																												raise ValueError(F'''Could not open {os.path.join(UpperCAmelCase__      ,					UpperCAmelCase__							)}.'''							) from e

							return _artifact



def   __lowerCAmelCase    (				)	->     List[str]:





							class    __A:

												def __init__(						self       :  int ,       __UpperCamelCase       :  str ):
																			lowerCamelCase_					=       name
																			lowerCamelCase_					=       []

												def __str__(						self       :  List[str] ):
																			return self.name



												def  lowercase__   (						self       :  Dict ,       __UpperCamelCase       :  str ):
																			self.paths.append({"""name""": self.name, """path""": path} )
							lowerCamelCase_					=       {}

							lowerCamelCase_					=       filter(os.path.isdir      ,					os.listdir()							)
							for directory in directories:
														lowerCamelCase_					=       directory
														if artifact_name not in _available_artifacts:
																					lowerCamelCase_					=       Artifact(UpperCAmelCase__							)

																					_available_artifacts[artifact_name].add_path(UpperCAmelCase__							)

							return _available_artifacts


if __name__ == "__main__":
		lowercase   =	get_job_links()
		lowercase   =	retrieve_available_artifacts()

		lowercase   =	collections.OrderedDict(
		    [
		        ('''*.py''', '''API Examples'''),
		        ('''*.md''', '''MD Examples'''),
		    ]
		)

		# This dict will contain all the information relative to each doc test category:
		# - failed: list of failed tests
		# - failures: dict in the format 'test': 'error_message'
		lowercase   =	{
		    v: {
		        '''failed''': [],
		        '''failures''': {},
		    }
		    for v in docs.values()
		}

		# Link to the GitHub Action job
		lowercase   =	github_actions_job_links.get('''run_doctests''')

		lowercase   =	available_artifacts['''doc_tests_gpu_test_reports'''].paths[0]
		lowercase   =	retrieve_artifact(artifact_path['''name'''])
		if "stats" in artifact:
				lowercase ,    lowercase ,    lowercase   =	handle_test_results(artifact['''stats'''])
				lowercase   =	failed
				lowercase   =	success
				lowercase   =	time_spent[1:-1] + ''', '''

				lowercase   =	extract_first_line_failure(artifact['''failures_short'''])
				for line in artifact["summary_short"].split('''\n'''):
						if re.search('''FAILED''', line):
								lowercase   =	line.replace('''FAILED ''', '''''')
								lowercase   =	line.split()[0].replace('''\n''', '''''')

								if "::" in line:
										lowercase ,    lowercase   =	line.split('''::''')
								else:
										lowercase ,    lowercase   =	line, line

								for file_regex in docs.keys():
										if fnmatch(file_path, file_regex):
												lowercase   =	docs[file_regex]
												doc_test_results[category]["failed"].append(test)

												lowercase   =	all_failures[test] if test in all_failures else '''N/A'''
												lowercase   =	failure
												break

		lowercase   =	Message('''🤗 Results of the doc tests.''', doc_test_results)
		message.post()
		message.post_reply()







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import 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
		lowercase   =	'''platform'''

		import jax
		import jax.numpy as jnp

		from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
		    FlaxBlenderbotSmallForConditionalGeneration,
		    FlaxBlenderbotSmallModel,
		    shift_tokens_right,
		)



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   Union[str, Any]      ,					UpperCAmelCase__					:   Any=None      ,					UpperCAmelCase__					:   Optional[Any]=None      ,					UpperCAmelCase__					:   List[str]=None      ,					UpperCAmelCase__					:   Optional[int]=None      ,					UpperCAmelCase__					:   List[str]=None      ,					UpperCAmelCase__					:   Dict=None      ,					)	->     List[Any]:
							if attention_mask is None:
														lowerCamelCase_					=       np.where(input_ids != config.pad_token_id      ,					1      ,					0							)
							if decoder_attention_mask is None:
														lowerCamelCase_					=       np.where(decoder_input_ids != config.pad_token_id      ,					1      ,					0							)
							if head_mask is None:
														lowerCamelCase_					=       np.ones((config.encoder_layers, config.encoder_attention_heads)							)
							if decoder_head_mask is None:
														lowerCamelCase_					=       np.ones((config.decoder_layers, config.decoder_attention_heads)							)
							if cross_attn_head_mask is None:
														lowerCamelCase_					=       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    __A:

					def __init__(						self       :  str ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  str=1_3 ,       __UpperCamelCase       :  List[str]=7 ,       __UpperCamelCase       :  Tuple=True ,       __UpperCamelCase       :  List[Any]=False ,       __UpperCamelCase       :  Dict=9_9 ,       __UpperCamelCase       :  Tuple=1_6 ,       __UpperCamelCase       :  Any=2 ,       __UpperCamelCase       :  Dict=4 ,       __UpperCamelCase       :  List[str]=4 ,       __UpperCamelCase       :  Optional[Any]="gelu" ,       __UpperCamelCase       :  List[str]=0.1 ,       __UpperCamelCase       :  str=0.1 ,       __UpperCamelCase       :  Union[str, Any]=3_2 ,       __UpperCamelCase       :  str=2 ,       __UpperCamelCase       :  Tuple=1 ,       __UpperCamelCase       :  Optional[Any]=0 ,       __UpperCamelCase       :  str=0.02 ,       ):
												lowerCamelCase_					=       parent
												lowerCamelCase_					=       batch_size
												lowerCamelCase_					=       seq_length
												lowerCamelCase_					=       is_training
												lowerCamelCase_					=       use_labels
												lowerCamelCase_					=       vocab_size
												lowerCamelCase_					=       hidden_size
												lowerCamelCase_					=       num_hidden_layers
												lowerCamelCase_					=       num_attention_heads
												lowerCamelCase_					=       intermediate_size
												lowerCamelCase_					=       hidden_act
												lowerCamelCase_					=       hidden_dropout_prob
												lowerCamelCase_					=       attention_probs_dropout_prob
												lowerCamelCase_					=       max_position_embeddings
												lowerCamelCase_					=       eos_token_id
												lowerCamelCase_					=       pad_token_id
												lowerCamelCase_					=       bos_token_id
												lowerCamelCase_					=       initializer_range

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

												lowerCamelCase_					=       shift_tokens_right(__UpperCamelCase ,       1 ,       2 )

												lowerCamelCase_					=       BlenderbotSmallConfig(
												    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=__UpperCamelCase ,       )
												lowerCamelCase_					=       prepare_blenderbot_inputs_dict(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
												return config, inputs_dict

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_	, lowerCamelCase_					=       self.prepare_config_and_inputs()
												return config, inputs_dict

					def  lowercase__   (						self       :  Tuple ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  str ):
												lowerCamelCase_					=       2_0
												lowerCamelCase_					=       model_class_name(__UpperCamelCase )

												lowerCamelCase_					=       model.encode(inputs_dict["""input_ids"""] )

												lowerCamelCase_	, lowerCamelCase_					=       (
												    inputs_dict["""decoder_input_ids"""],
												    inputs_dict["""decoder_attention_mask"""],
												)

												lowerCamelCase_					=       model.init_cache(decoder_input_ids.shape[0] ,       __UpperCamelCase ,       __UpperCamelCase )
												lowerCamelCase_					=       jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,       dtype="""i4""" )

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

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

												lowerCamelCase_					=       model.decode(__UpperCamelCase ,       __UpperCamelCase )

												lowerCamelCase_					=       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  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  List[str] ):
												lowerCamelCase_					=       2_0
												lowerCamelCase_					=       model_class_name(__UpperCamelCase )

												lowerCamelCase_					=       model.encode(inputs_dict["""input_ids"""] )

												lowerCamelCase_	, lowerCamelCase_					=       (
												    inputs_dict["""decoder_input_ids"""],
												    inputs_dict["""decoder_attention_mask"""],
												)

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

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

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

												lowerCamelCase_					=       model.decode(__UpperCamelCase ,       __UpperCamelCase ,       decoder_attention_mask=__UpperCamelCase )

												lowerCamelCase_					=       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    __A(					unittest.TestCase							):
					SCREAMING_SNAKE_CASE           =     9_9

					def  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       np.array(
												    [
												        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
												        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
												        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
												        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
												        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
												        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
												        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
												        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
												        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
												        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
												        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
												        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
												        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
												    ] ,       dtype=np.intaa ,       )

												lowerCamelCase_					=       input_ids.shape[0]
												lowerCamelCase_					=       BlenderbotSmallConfig(
												    vocab_size=self.vocab_size ,       d_model=2_4 ,       encoder_layers=2 ,       decoder_layers=2 ,       encoder_attention_heads=2 ,       decoder_attention_heads=2 ,       encoder_ffn_dim=3_2 ,       decoder_ffn_dim=3_2 ,       max_position_embeddings=4_8 ,       eos_token_id=2 ,       pad_token_id=1 ,       bos_token_id=0 ,       )
												return config, input_ids, batch_size

					def  lowercase__   (						self       :  List[Any] ):
												lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_					=       self._get_config_and_data()
												lowerCamelCase_					=       FlaxBlenderbotSmallForConditionalGeneration(__UpperCamelCase )
												lowerCamelCase_					=       lm_model(input_ids=__UpperCamelCase )
												lowerCamelCase_					=       (batch_size, input_ids.shape[1], config.vocab_size)
												self.assertEqual(outputs["""logits"""].shape ,       __UpperCamelCase )

					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       BlenderbotSmallConfig(
												    vocab_size=self.vocab_size ,       d_model=1_4 ,       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=4_8 ,       )
												lowerCamelCase_					=       FlaxBlenderbotSmallForConditionalGeneration(__UpperCamelCase )
												lowerCamelCase_					=       np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]] ,       dtype=np.intaa )
												lowerCamelCase_					=       np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]] ,       dtype=np.intaa )
												lowerCamelCase_					=       lm_model(input_ids=__UpperCamelCase ,       decoder_input_ids=__UpperCamelCase )
												lowerCamelCase_					=       (*summary.shape, config.vocab_size)
												self.assertEqual(outputs["""logits"""].shape ,       __UpperCamelCase )



					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]] ,       dtype=np.intaa )
												lowerCamelCase_					=       shift_tokens_right(__UpperCamelCase ,       1 ,       2 )
												lowerCamelCase_					=       np.equal(__UpperCamelCase ,       1 ).astype(np.floataa ).sum()
												lowerCamelCase_					=       np.equal(__UpperCamelCase ,       1 ).astype(np.floataa ).sum()
												self.assertEqual(shifted.shape ,       input_ids.shape )
												self.assertEqual(__UpperCamelCase ,       n_pad_before - 1 )
												self.assertTrue(np.equal(shifted[:, 0] ,       2 ).all() )





@require_flax
class    __A(					UpperCAmelCase  ,		unittest.TestCase  ,		UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     True
					SCREAMING_SNAKE_CASE           =     (
					    (
					        FlaxBlenderbotSmallModel,
					        FlaxBlenderbotSmallForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE           =     (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

					def  lowercase__   (						self       :  Optional[Any] ):
												lowerCamelCase_					=       FlaxBlenderbotSmallModelTester(self )

					def  lowercase__   (						self       :  str ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																			self.model_tester.check_use_cache_forward(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_	, lowerCamelCase_					=       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(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__ ):
																										lowerCamelCase_					=       self._prepare_for_class(__UpperCamelCase ,       __UpperCamelCase )
																										lowerCamelCase_					=       model_class(__UpperCamelCase )

																										@jax.jit
																										def encode_jitted(__UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Dict=None ,       **__UpperCamelCase       :  Tuple ):
																																	return model.encode(input_ids=__UpperCamelCase ,       attention_mask=__UpperCamelCase )

																										with self.subTest("""JIT Enabled""" ):
																																	lowerCamelCase_					=       encode_jitted(**__UpperCamelCase ).to_tuple()

																										with self.subTest("""JIT Disabled""" ):
																																	with jax.disable_jit():
																																								lowerCamelCase_					=       encode_jitted(**__UpperCamelCase ).to_tuple()

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

					def  lowercase__   (						self       :  Any ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			with self.subTest(model_class.__name__ ):
																										lowerCamelCase_					=       model_class(__UpperCamelCase )
																										lowerCamelCase_					=       model.encode(inputs_dict["""input_ids"""] ,       inputs_dict["""attention_mask"""] )

																										lowerCamelCase_					=       {
																										    """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       :  int ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  Union[str, Any] ):
																																	return model.decode(
																																	    decoder_input_ids=__UpperCamelCase ,       decoder_attention_mask=__UpperCamelCase ,       encoder_outputs=__UpperCamelCase ,       )

																										with self.subTest("""JIT Enabled""" ):
																																	lowerCamelCase_					=       decode_jitted(**__UpperCamelCase ).to_tuple()

																										with self.subTest("""JIT Disabled""" ):
																																	with jax.disable_jit():
																																								lowerCamelCase_					=       decode_jitted(**__UpperCamelCase ).to_tuple()

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



					@slow
					def  lowercase__   (						self       :  str ):
												for model_class_name in self.all_model_classes:
																			lowerCamelCase_					=       model_class_name.from_pretrained("""facebook/blenderbot_small-90M""" )
																			# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
																			lowerCamelCase_					=       np.ones((1, 1) ) * model.config.eos_token_id
																			lowerCamelCase_					=       model(__UpperCamelCase )
																			self.assertIsNotNone(__UpperCamelCase )





